pub mod calib3d {
	//! # Camera Calibration and 3D Reconstruction
	//!
	//! The functions in this section use a so-called pinhole camera model. The view of a scene
	//! is obtained by projecting a scene's 3D point ![inline formula](https://latex.codecogs.com/png.latex?P%5Fw) into the image plane using a perspective
	//! transformation which forms the corresponding pixel ![inline formula](https://latex.codecogs.com/png.latex?p). Both ![inline formula](https://latex.codecogs.com/png.latex?P%5Fw) and ![inline formula](https://latex.codecogs.com/png.latex?p) are
	//! represented in homogeneous coordinates, i.e. as 3D and 2D homogeneous vector respectively. You will
	//! find a brief introduction to projective geometry, homogeneous vectors and homogeneous
	//! transformations at the end of this section's introduction. For more succinct notation, we often drop
	//! the 'homogeneous' and say vector instead of homogeneous vector.
	//!
	//! The distortion-free projective transformation given by a  pinhole camera model is shown below.
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?s%20%5C%3B%20p%20%3D%20A%20%5Cbegin%7Bbmatrix%7D%20R%7Ct%20%5Cend%7Bbmatrix%7D%20P%5Fw%2C)
	//!
	//! where ![inline formula](https://latex.codecogs.com/png.latex?P%5Fw) is a 3D point expressed with respect to the world coordinate system,
	//! ![inline formula](https://latex.codecogs.com/png.latex?p) is a 2D pixel in the image plane, ![inline formula](https://latex.codecogs.com/png.latex?A) is the camera intrinsic matrix,
	//! ![inline formula](https://latex.codecogs.com/png.latex?R) and ![inline formula](https://latex.codecogs.com/png.latex?t) are the rotation and translation that describe the change of coordinates from
	//! world to camera coordinate systems (or camera frame) and ![inline formula](https://latex.codecogs.com/png.latex?s) is the projective transformation's
	//! arbitrary scaling and not part of the camera model.
	//!
	//! The camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?A) (notation used as in [Zhang2000](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Zhang2000) and also generally notated
	//! as ![inline formula](https://latex.codecogs.com/png.latex?K)) projects 3D points given in the camera coordinate system to 2D pixel coordinates, i.e.
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?p%20%3D%20A%20P%5Fc%2E)
	//!
	//! The camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?A) is composed of the focal lengths ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5Fy), which are
	//! expressed in pixel units, and the principal point ![inline formula](https://latex.codecogs.com/png.latex?%28c%5Fx%2C%20c%5Fy%29), that is usually close to the
	//! image center:
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?A%20%3D%20%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D%2C)
	//!
	//! and thus
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?s%20%5Cbegin%7Bbmatrix%7D%20u%5C%5C%20v%5C%5C%201%20%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D%20%5Cbegin%7Bbmatrix%7D%20X%5Fc%5C%5C%20Y%5Fc%5C%5C%20Z%5Fc%20%5Cend%7Bbmatrix%7D%2E)
	//!
	//! The matrix of intrinsic parameters does not depend on the scene viewed. So, once estimated, it can
	//! be re-used as long as the focal length is fixed (in case of a zoom lens). Thus, if an image from the
	//! camera is scaled by a factor, all of these parameters need to be scaled (multiplied/divided,
	//! respectively) by the same factor.
	//!
	//! The joint rotation-translation matrix ![inline formula](https://latex.codecogs.com/png.latex?%5BR%7Ct%5D) is the matrix product of a projective
	//! transformation and a homogeneous transformation. The 3-by-4 projective transformation maps 3D points
	//! represented in camera coordinates to 2D points in the image plane and represented in normalized
	//! camera coordinates ![inline formula](https://latex.codecogs.com/png.latex?x%27%20%3D%20X%5Fc%20%2F%20Z%5Fc) and ![inline formula](https://latex.codecogs.com/png.latex?y%27%20%3D%20Y%5Fc%20%2F%20Z%5Fc):
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?Z%5Fc%20%5Cbegin%7Bbmatrix%7D%0Ax%27%20%5C%5C%0Ay%27%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0A1%20%26%200%20%26%200%20%26%200%20%5C%5C%0A0%20%26%201%20%26%200%20%26%200%20%5C%5C%0A0%20%26%200%20%26%201%20%26%200%0A%5Cend%7Bbmatrix%7D%0A%5Cbegin%7Bbmatrix%7D%0AX%5Fc%20%5C%5C%0AY%5Fc%20%5C%5C%0AZ%5Fc%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%2E)
	//!
	//! The homogeneous transformation is encoded by the extrinsic parameters ![inline formula](https://latex.codecogs.com/png.latex?R) and ![inline formula](https://latex.codecogs.com/png.latex?t) and
	//! represents the change of basis from world coordinate system ![inline formula](https://latex.codecogs.com/png.latex?w) to the camera coordinate sytem
	//! ![inline formula](https://latex.codecogs.com/png.latex?c). Thus, given the representation of the point ![inline formula](https://latex.codecogs.com/png.latex?P) in world coordinates, ![inline formula](https://latex.codecogs.com/png.latex?P%5Fw), we
	//! obtain ![inline formula](https://latex.codecogs.com/png.latex?P)'s representation in the camera coordinate system, ![inline formula](https://latex.codecogs.com/png.latex?P%5Fc), by
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?P%5Fc%20%3D%20%5Cbegin%7Bbmatrix%7D%0AR%20%26%20t%20%5C%5C%0A0%20%26%201%0A%5Cend%7Bbmatrix%7D%20P%5Fw%2C)
	//!
	//! This homogeneous transformation is composed out of ![inline formula](https://latex.codecogs.com/png.latex?R), a 3-by-3 rotation matrix, and ![inline formula](https://latex.codecogs.com/png.latex?t), a
	//! 3-by-1 translation vector:
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0AR%20%26%20t%20%5C%5C%0A0%20%26%201%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0Ar%5F%7B11%7D%20%26%20r%5F%7B12%7D%20%26%20r%5F%7B13%7D%20%26%20t%5Fx%20%5C%5C%0Ar%5F%7B21%7D%20%26%20r%5F%7B22%7D%20%26%20r%5F%7B23%7D%20%26%20t%5Fy%20%5C%5C%0Ar%5F%7B31%7D%20%26%20r%5F%7B32%7D%20%26%20r%5F%7B33%7D%20%26%20t%5Fz%20%5C%5C%0A0%20%26%200%20%26%200%20%26%201%0A%5Cend%7Bbmatrix%7D%2C%0A)
	//!
	//! and therefore
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0AX%5Fc%20%5C%5C%0AY%5Fc%20%5C%5C%0AZ%5Fc%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0Ar%5F%7B11%7D%20%26%20r%5F%7B12%7D%20%26%20r%5F%7B13%7D%20%26%20t%5Fx%20%5C%5C%0Ar%5F%7B21%7D%20%26%20r%5F%7B22%7D%20%26%20r%5F%7B23%7D%20%26%20t%5Fy%20%5C%5C%0Ar%5F%7B31%7D%20%26%20r%5F%7B32%7D%20%26%20r%5F%7B33%7D%20%26%20t%5Fz%20%5C%5C%0A0%20%26%200%20%26%200%20%26%201%0A%5Cend%7Bbmatrix%7D%0A%5Cbegin%7Bbmatrix%7D%0AX%5Fw%20%5C%5C%0AY%5Fw%20%5C%5C%0AZ%5Fw%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%2E)
	//!
	//! Combining the projective transformation and the homogeneous transformation, we obtain the projective
	//! transformation that maps 3D points in world coordinates into 2D points in the image plane and in
	//! normalized camera coordinates:
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?Z%5Fc%20%5Cbegin%7Bbmatrix%7D%0Ax%27%20%5C%5C%0Ay%27%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%20R%7Ct%20%5Cend%7Bbmatrix%7D%20%5Cbegin%7Bbmatrix%7D%0AX%5Fw%20%5C%5C%0AY%5Fw%20%5C%5C%0AZ%5Fw%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0Ar%5F%7B11%7D%20%26%20r%5F%7B12%7D%20%26%20r%5F%7B13%7D%20%26%20t%5Fx%20%5C%5C%0Ar%5F%7B21%7D%20%26%20r%5F%7B22%7D%20%26%20r%5F%7B23%7D%20%26%20t%5Fy%20%5C%5C%0Ar%5F%7B31%7D%20%26%20r%5F%7B32%7D%20%26%20r%5F%7B33%7D%20%26%20t%5Fz%0A%5Cend%7Bbmatrix%7D%0A%5Cbegin%7Bbmatrix%7D%0AX%5Fw%20%5C%5C%0AY%5Fw%20%5C%5C%0AZ%5Fw%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%2C)
	//!
	//! with ![inline formula](https://latex.codecogs.com/png.latex?x%27%20%3D%20X%5Fc%20%2F%20Z%5Fc) and ![inline formula](https://latex.codecogs.com/png.latex?y%27%20%3D%20Y%5Fc%20%2F%20Z%5Fc). Putting the equations for instrincs and extrinsics together, we can write out
	//! ![inline formula](https://latex.codecogs.com/png.latex?s%20%5C%3B%20p%20%3D%20A%20%5Cbegin%7Bbmatrix%7D%20R%7Ct%20%5Cend%7Bbmatrix%7D%20P%5Fw) as
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?s%20%5Cbegin%7Bbmatrix%7D%20u%5C%5C%20v%5C%5C%201%20%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D%0A%5Cbegin%7Bbmatrix%7D%0Ar%5F%7B11%7D%20%26%20r%5F%7B12%7D%20%26%20r%5F%7B13%7D%20%26%20t%5Fx%20%5C%5C%0Ar%5F%7B21%7D%20%26%20r%5F%7B22%7D%20%26%20r%5F%7B23%7D%20%26%20t%5Fy%20%5C%5C%0Ar%5F%7B31%7D%20%26%20r%5F%7B32%7D%20%26%20r%5F%7B33%7D%20%26%20t%5Fz%0A%5Cend%7Bbmatrix%7D%0A%5Cbegin%7Bbmatrix%7D%0AX%5Fw%20%5C%5C%0AY%5Fw%20%5C%5C%0AZ%5Fw%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%2E)
	//!
	//! If ![inline formula](https://latex.codecogs.com/png.latex?Z%5Fc%20%5Cne%200), the transformation above is equivalent to the following,
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0Au%20%5C%5C%0Av%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0Af%5Fx%20X%5Fc%2FZ%5Fc%20%2B%20c%5Fx%20%5C%5C%0Af%5Fy%20Y%5Fc%2FZ%5Fc%20%2B%20c%5Fy%0A%5Cend%7Bbmatrix%7D)
	//!
	//! with
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20X%5Fc%5C%5C%20Y%5Fc%5C%5C%20Z%5Fc%20%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0AR%7Ct%0A%5Cend%7Bbmatrix%7D%20%5Cbegin%7Bbmatrix%7D%0AX%5Fw%20%5C%5C%0AY%5Fw%20%5C%5C%0AZ%5Fw%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%2E)
	//!
	//! The following figure illustrates the pinhole camera model.
	//!
	//! ![Pinhole camera model](https://docs.opencv.org/4.12.0/pinhole_camera_model.png)
	//!
	//! Real lenses usually have some distortion, mostly radial distortion, and slight tangential distortion.
	//! So, the above model is extended as:
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0Au%20%5C%5C%0Av%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0Af%5Fx%20x%27%27%20%2B%20c%5Fx%20%5C%5C%0Af%5Fy%20y%27%27%20%2B%20c%5Fy%0A%5Cend%7Bbmatrix%7D)
	//!
	//! where
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0Ax%27%27%20%5C%5C%0Ay%27%27%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0Ax%27%20%5Cfrac%7B1%20%2B%20k%5F1%20r%5E2%20%2B%20k%5F2%20r%5E4%20%2B%20k%5F3%20r%5E6%7D%7B1%20%2B%20k%5F4%20r%5E2%20%2B%20k%5F5%20r%5E4%20%2B%20k%5F6%20r%5E6%7D%20%2B%202%20p%5F1%20x%27%20y%27%20%2B%20p%5F2%28r%5E2%20%2B%202%20x%27%5E2%29%20%2B%20s%5F1%20r%5E2%20%2B%20s%5F2%20r%5E4%20%5C%5C%0Ay%27%20%5Cfrac%7B1%20%2B%20k%5F1%20r%5E2%20%2B%20k%5F2%20r%5E4%20%2B%20k%5F3%20r%5E6%7D%7B1%20%2B%20k%5F4%20r%5E2%20%2B%20k%5F5%20r%5E4%20%2B%20k%5F6%20r%5E6%7D%20%2B%20p%5F1%20%28r%5E2%20%2B%202%20y%27%5E2%29%20%2B%202%20p%5F2%20x%27%20y%27%20%2B%20s%5F3%20r%5E2%20%2B%20s%5F4%20r%5E4%20%5C%5C%0A%5Cend%7Bbmatrix%7D)
	//!
	//! with
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?r%5E2%20%3D%20x%27%5E2%20%2B%20y%27%5E2)
	//!
	//! and
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0Ax%27%5C%5C%0Ay%27%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0AX%5Fc%2FZ%5Fc%20%5C%5C%0AY%5Fc%2FZ%5Fc%0A%5Cend%7Bbmatrix%7D%2C)
	//!
	//! if ![inline formula](https://latex.codecogs.com/png.latex?Z%5Fc%20%5Cne%200).
	//!
	//! The distortion parameters are the radial coefficients ![inline formula](https://latex.codecogs.com/png.latex?k%5F1), ![inline formula](https://latex.codecogs.com/png.latex?k%5F2), ![inline formula](https://latex.codecogs.com/png.latex?k%5F3), ![inline formula](https://latex.codecogs.com/png.latex?k%5F4), ![inline formula](https://latex.codecogs.com/png.latex?k%5F5), and ![inline formula](https://latex.codecogs.com/png.latex?k%5F6)
	//! ,![inline formula](https://latex.codecogs.com/png.latex?p%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) are the tangential distortion coefficients, and ![inline formula](https://latex.codecogs.com/png.latex?s%5F1), ![inline formula](https://latex.codecogs.com/png.latex?s%5F2), ![inline formula](https://latex.codecogs.com/png.latex?s%5F3), and ![inline formula](https://latex.codecogs.com/png.latex?s%5F4),
	//! are the thin prism distortion coefficients. Higher-order coefficients are not considered in OpenCV.
	//!
	//! The next figures show two common types of radial distortion: barrel distortion
	//! (![inline formula](https://latex.codecogs.com/png.latex?%201%20%2B%20k%5F1%20r%5E2%20%2B%20k%5F2%20r%5E4%20%2B%20k%5F3%20r%5E6%20) monotonically decreasing)
	//! and pincushion distortion (![inline formula](https://latex.codecogs.com/png.latex?%201%20%2B%20k%5F1%20r%5E2%20%2B%20k%5F2%20r%5E4%20%2B%20k%5F3%20r%5E6%20) monotonically increasing).
	//! Radial distortion is always monotonic for real lenses,
	//! and if the estimator produces a non-monotonic result,
	//! this should be considered a calibration failure.
	//! More generally, radial distortion must be monotonic and the distortion function must be bijective.
	//! A failed estimation result may look deceptively good near the image center
	//! but will work poorly in e.g. AR/SFM applications.
	//! The optimization method used in OpenCV camera calibration does not include these constraints as
	//! the framework does not support the required integer programming and polynomial inequalities.
	//! See [issue #15992](https://github.com/opencv/opencv/issues/15992) for additional information.
	//!
	//! ![](https://docs.opencv.org/4.12.0/distortion_examples.png)
	//! ![](https://docs.opencv.org/4.12.0/distortion_examples2.png)
	//!
	//! In some cases, the image sensor may be tilted in order to focus an oblique plane in front of the
	//! camera (Scheimpflug principle). This can be useful for particle image velocimetry (PIV) or
	//! triangulation with a laser fan. The tilt causes a perspective distortion of ![inline formula](https://latex.codecogs.com/png.latex?x%27%27) and
	//! ![inline formula](https://latex.codecogs.com/png.latex?y%27%27). This distortion can be modeled in the following way, see e.g. [Louhichi07](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Louhichi07).
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0Au%20%5C%5C%0Av%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0Af%5Fx%20x%27%27%27%20%2B%20c%5Fx%20%5C%5C%0Af%5Fy%20y%27%27%27%20%2B%20c%5Fy%0A%5Cend%7Bbmatrix%7D%2C)
	//!
	//! where
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?s%5Cbegin%7Bbmatrix%7D%20x%27%27%27%5C%5C%20y%27%27%27%5C%5C%201%20%5Cend%7Bbmatrix%7D%20%3D%0A%5Cvecthreethree%7BR%5F%7B33%7D%28%5Ctau%5Fx%2C%20%5Ctau%5Fy%29%7D%7B0%7D%7B%2DR%5F%7B13%7D%28%5Ctau%5Fx%2C%20%5Ctau%5Fy%29%7D%0A%7B0%7D%7BR%5F%7B33%7D%28%5Ctau%5Fx%2C%20%5Ctau%5Fy%29%7D%7B%2DR%5F%7B23%7D%28%5Ctau%5Fx%2C%20%5Ctau%5Fy%29%7D%0A%7B0%7D%7B0%7D%7B1%7D%20R%28%5Ctau%5Fx%2C%20%5Ctau%5Fy%29%20%5Cbegin%7Bbmatrix%7D%20x%27%27%5C%5C%20y%27%27%5C%5C%201%20%5Cend%7Bbmatrix%7D)
	//!
	//! and the matrix ![inline formula](https://latex.codecogs.com/png.latex?R%28%5Ctau%5Fx%2C%20%5Ctau%5Fy%29) is defined by two rotations with angular parameter
	//! ![inline formula](https://latex.codecogs.com/png.latex?%5Ctau%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?%5Ctau%5Fy), respectively,
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%0AR%28%5Ctau%5Fx%2C%20%5Ctau%5Fy%29%20%3D%0A%5Cbegin%7Bbmatrix%7D%20%5Ccos%28%5Ctau%5Fy%29%20%26%200%20%26%20%2D%5Csin%28%5Ctau%5Fy%29%5C%5C%200%20%26%201%20%26%200%5C%5C%20%5Csin%28%5Ctau%5Fy%29%20%26%200%20%26%20%5Ccos%28%5Ctau%5Fy%29%20%5Cend%7Bbmatrix%7D%0A%5Cbegin%7Bbmatrix%7D%201%20%26%200%20%26%200%5C%5C%200%20%26%20%5Ccos%28%5Ctau%5Fx%29%20%26%20%5Csin%28%5Ctau%5Fx%29%5C%5C%200%20%26%20%2D%5Csin%28%5Ctau%5Fx%29%20%26%20%5Ccos%28%5Ctau%5Fx%29%20%5Cend%7Bbmatrix%7D%20%3D%0A%5Cbegin%7Bbmatrix%7D%20%5Ccos%28%5Ctau%5Fy%29%20%26%20%5Csin%28%5Ctau%5Fy%29%5Csin%28%5Ctau%5Fx%29%20%26%20%2D%5Csin%28%5Ctau%5Fy%29%5Ccos%28%5Ctau%5Fx%29%5C%5C%200%20%26%20%5Ccos%28%5Ctau%5Fx%29%20%26%20%5Csin%28%5Ctau%5Fx%29%5C%5C%20%5Csin%28%5Ctau%5Fy%29%20%26%20%2D%5Ccos%28%5Ctau%5Fy%29%5Csin%28%5Ctau%5Fx%29%20%26%20%5Ccos%28%5Ctau%5Fy%29%5Ccos%28%5Ctau%5Fx%29%20%5Cend%7Bbmatrix%7D%2E%0A)
	//!
	//! In the functions below the coefficients are passed or returned as
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%28k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%5B%2C%20k%5F3%5B%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%20%5B%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%20s%5F4%5B%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%5D%5D%5D%5D%29)
	//!
	//! vector. That is, if the vector contains four elements, it means that ![inline formula](https://latex.codecogs.com/png.latex?k%5F3%3D0) . The distortion
	//! coefficients do not depend on the scene viewed. Thus, they also belong to the intrinsic camera
	//! parameters. And they remain the same regardless of the captured image resolution. If, for example, a
	//! camera has been calibrated on images of 320 x 240 resolution, absolutely the same distortion
	//! coefficients can be used for 640 x 480 images from the same camera while ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx), ![inline formula](https://latex.codecogs.com/png.latex?f%5Fy),
	//! ![inline formula](https://latex.codecogs.com/png.latex?c%5Fx), and ![inline formula](https://latex.codecogs.com/png.latex?c%5Fy) need to be scaled appropriately.
	//!
	//! The functions below use the above model to do the following:
	//!
	//! *   Project 3D points to the image plane given intrinsic and extrinsic parameters.
	//! *   Compute extrinsic parameters given intrinsic parameters, a few 3D points, and their
	//! projections.
	//! *   Estimate intrinsic and extrinsic camera parameters from several views of a known calibration
	//! pattern (every view is described by several 3D-2D point correspondences).
	//! *   Estimate the relative position and orientation of the stereo camera "heads" and compute the
	//! *rectification* transformation that makes the camera optical axes parallel.
	//!
	//! <B> Homogeneous Coordinates </B><br>
	//! Homogeneous Coordinates are a system of coordinates that are used in projective geometry. Their use
	//! allows to represent points at infinity by finite coordinates and simplifies formulas when compared
	//! to the cartesian counterparts, e.g. they have the advantage that affine transformations can be
	//! expressed as linear homogeneous transformation.
	//!
	//! One obtains the homogeneous vector ![inline formula](https://latex.codecogs.com/png.latex?P%5Fh) by appending a 1 along an n-dimensional cartesian
	//! vector ![inline formula](https://latex.codecogs.com/png.latex?P) e.g. for a 3D cartesian vector the mapping ![inline formula](https://latex.codecogs.com/png.latex?P%20%5Crightarrow%20P%5Fh) is:
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0AX%20%5C%5C%0AY%20%5C%5C%0AZ%0A%5Cend%7Bbmatrix%7D%20%5Crightarrow%20%5Cbegin%7Bbmatrix%7D%0AX%20%5C%5C%0AY%20%5C%5C%0AZ%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%2E)
	//!
	//! For the inverse mapping ![inline formula](https://latex.codecogs.com/png.latex?P%5Fh%20%5Crightarrow%20P), one divides all elements of the homogeneous vector
	//! by its last element, e.g. for a 3D homogeneous vector one gets its 2D cartesian counterpart by:
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0AX%20%5C%5C%0AY%20%5C%5C%0AW%0A%5Cend%7Bbmatrix%7D%20%5Crightarrow%20%5Cbegin%7Bbmatrix%7D%0AX%20%2F%20W%20%5C%5C%0AY%20%2F%20W%0A%5Cend%7Bbmatrix%7D%2C)
	//!
	//! if ![inline formula](https://latex.codecogs.com/png.latex?W%20%5Cne%200).
	//!
	//! Due to this mapping, all multiples ![inline formula](https://latex.codecogs.com/png.latex?k%20P%5Fh), for ![inline formula](https://latex.codecogs.com/png.latex?k%20%5Cne%200), of a homogeneous point represent
	//! the same point ![inline formula](https://latex.codecogs.com/png.latex?P%5Fh). An intuitive understanding of this property is that under a projective
	//! transformation, all multiples of ![inline formula](https://latex.codecogs.com/png.latex?P%5Fh) are mapped to the same point. This is the physical
	//! observation one does for pinhole cameras, as all points along a ray through the camera's pinhole are
	//! projected to the same image point, e.g. all points along the red ray in the image of the pinhole
	//! camera model above would be mapped to the same image coordinate. This property is also the source
	//! for the scale ambiguity s in the equation of the pinhole camera model.
	//!
	//! As mentioned, by using homogeneous coordinates we can express any change of basis parameterized by
	//! ![inline formula](https://latex.codecogs.com/png.latex?R) and ![inline formula](https://latex.codecogs.com/png.latex?t) as a linear transformation, e.g. for the change of basis from coordinate system
	//! 0 to coordinate system 1 becomes:
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?P%5F1%20%3D%20R%20P%5F0%20%2B%20t%20%5Crightarrow%20P%5F%7Bh%5F1%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0AR%20%26%20t%20%5C%5C%0A0%20%26%201%0A%5Cend%7Bbmatrix%7D%20P%5F%7Bh%5F0%7D%2E)
	//!
	//! <B> Homogeneous Transformations, Object frame / Camera frame </B><br>
	//! Change of basis or computing the 3D coordinates from one frame to another frame can be achieved easily using
	//! the following notation:
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cmathbf%7BX%7D%5Fc%20%3D%20%5Chspace%7B0%2E2em%7D%0A%7B%7D%5E%7Bc%7D%5Cmathbf%7BT%7D%5Fo%20%5Chspace%7B0%2E2em%7D%20%5Cmathbf%7BX%7D%5Fo%0A)
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Bbmatrix%7D%0AX%5Fc%20%5C%5C%0AY%5Fc%20%5C%5C%0AZ%5Fc%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%20%3D%0A%5Cbegin%7Bbmatrix%7D%0A%7B%7D%5E%7Bc%7D%5Cmathbf%7BR%7D%5Fo%20%26%20%7B%7D%5E%7Bc%7D%5Cmathbf%7Bt%7D%5Fo%20%5C%5C%0A0%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%5Cend%7Bbmatrix%7D%0A%5Cbegin%7Bbmatrix%7D%0AX%5Fo%20%5C%5C%0AY%5Fo%20%5C%5C%0AZ%5Fo%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%0A)
	//!
	//! For a 3D points (![inline formula](https://latex.codecogs.com/png.latex?%20%5Cmathbf%7BX%7D%5Fo%20)) expressed in the object frame, the homogeneous transformation matrix
	//! ![inline formula](https://latex.codecogs.com/png.latex?%20%7B%7D%5E%7Bc%7D%5Cmathbf%7BT%7D%5Fo%20) allows computing the corresponding coordinate (![inline formula](https://latex.codecogs.com/png.latex?%20%5Cmathbf%7BX%7D%5Fc%20)) in the camera frame.
	//! This transformation matrix is composed of a 3x3 rotation matrix ![inline formula](https://latex.codecogs.com/png.latex?%20%7B%7D%5E%7Bc%7D%5Cmathbf%7BR%7D%5Fo%20) and a 3x1 translation vector
	//! ![inline formula](https://latex.codecogs.com/png.latex?%20%7B%7D%5E%7Bc%7D%5Cmathbf%7Bt%7D%5Fo%20).
	//! The 3x1 translation vector ![inline formula](https://latex.codecogs.com/png.latex?%20%7B%7D%5E%7Bc%7D%5Cmathbf%7Bt%7D%5Fo%20) is the position of the object frame in the camera frame and the
	//! 3x3 rotation matrix ![inline formula](https://latex.codecogs.com/png.latex?%20%7B%7D%5E%7Bc%7D%5Cmathbf%7BR%7D%5Fo%20) the orientation of the object frame in the camera frame.
	//!
	//! With this simple notation, it is easy to chain the transformations. For instance, to compute the 3D coordinates of a point
	//! expressed in the object frame in the world frame can be done with:
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cmathbf%7BX%7D%5Fw%20%3D%20%5Chspace%7B0%2E2em%7D%0A%7B%7D%5E%7Bw%7D%5Cmathbf%7BT%7D%5Fc%20%5Chspace%7B0%2E2em%7D%20%7B%7D%5E%7Bc%7D%5Cmathbf%7BT%7D%5Fo%20%5Chspace%7B0%2E2em%7D%0A%5Cmathbf%7BX%7D%5Fo%20%3D%0A%7B%7D%5E%7Bw%7D%5Cmathbf%7BT%7D%5Fo%20%5Chspace%7B0%2E2em%7D%20%5Cmathbf%7BX%7D%5Fo%0A)
	//!
	//! Similarly, computing the inverse transformation can be done with:
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cmathbf%7BX%7D%5Fo%20%3D%20%5Chspace%7B0%2E2em%7D%0A%7B%7D%5E%7Bo%7D%5Cmathbf%7BT%7D%5Fc%20%5Chspace%7B0%2E2em%7D%20%5Cmathbf%7BX%7D%5Fc%20%3D%0A%5Cleft%28%20%7B%7D%5E%7Bc%7D%5Cmathbf%7BT%7D%5Fo%20%5Cright%29%5E%7B%2D1%7D%20%5Chspace%7B0%2E2em%7D%20%5Cmathbf%7BX%7D%5Fc%0A)
	//!
	//! The inverse of an homogeneous transformation matrix is then:
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%0A%7B%7D%5E%7Bo%7D%5Cmathbf%7BT%7D%5Fc%20%3D%20%5Cleft%28%20%7B%7D%5E%7Bc%7D%5Cmathbf%7BT%7D%5Fo%20%5Cright%29%5E%7B%2D1%7D%20%3D%0A%5Cbegin%7Bbmatrix%7D%0A%7B%7D%5E%7Bc%7D%5Cmathbf%7BR%7D%5E%7B%5Ctop%7D%5Fo%20%26%20%2D%20%5Chspace%7B0%2E2em%7D%20%7B%7D%5E%7Bc%7D%5Cmathbf%7BR%7D%5E%7B%5Ctop%7D%5Fo%20%5Chspace%7B0%2E2em%7D%20%7B%7D%5E%7Bc%7D%5Cmathbf%7Bt%7D%5Fo%20%5C%5C%0A0%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%5Cend%7Bbmatrix%7D%0A)
	//!
	//! One can note that the inverse of a 3x3 rotation matrix is directly its matrix transpose.
	//!
	//! ![Perspective projection, from object to camera frame](https://docs.opencv.org/4.12.0/pinhole_homogeneous_transformation.png)
	//!
	//! This figure summarizes the whole process. The object pose returned for instance by the [solvePnP] function
	//! or pose from fiducial marker detection is this ![inline formula](https://latex.codecogs.com/png.latex?%20%7B%7D%5E%7Bc%7D%5Cmathbf%7BT%7D%5Fo%20) transformation.
	//!
	//! The camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%20%5Cmathbf%7BK%7D%20) allows projecting the 3D point expressed in the camera frame onto the image plane
	//! assuming a perspective projection model (pinhole camera model). Image coordinates extracted from classical image processing functions
	//! assume a (u,v) top-left coordinates frame.
	//!
	//! \note
	//! - for an online video course on this topic, see for instance:
	//!   - ["3.3.1. Homogeneous Transformation Matrices", Modern Robotics, Kevin M. Lynch and Frank C. Park](https://modernrobotics.northwestern.edu/nu-gm-book-resource/3-3-1-homogeneous-transformation-matrices/)
	//! - the 3x3 rotation matrix is composed of 9 values but describes a 3 dof transformation
	//! - some additional properties of the 3x3 rotation matrix are:
	//!   - ![inline formula](https://latex.codecogs.com/png.latex?%20%5Cmathrm%7Bdet%7D%20%5Cleft%28%20%5Cmathbf%7BR%7D%20%5Cright%29%20%3D%201%20)
	//!   - ![inline formula](https://latex.codecogs.com/png.latex?%20%5Cmathbf%7BR%7D%20%5Cmathbf%7BR%7D%5E%7B%5Ctop%7D%20%3D%20%5Cmathbf%7BR%7D%5E%7B%5Ctop%7D%20%5Cmathbf%7BR%7D%20%3D%20%5Cmathrm%7BI%7D%5F%7B3%20%5Ctimes%203%7D%20)
	//!   - interpolating rotation can be done using the [Slerp (spherical linear interpolation)](https://en.wikipedia.org/wiki/Slerp) method
	//! - quick conversions between the different rotation formalisms can be done using this [online tool](https://www.andre-gaschler.com/rotationconverter/)
	//!
	//! <B> Intrinsic parameters from camera lens specifications </B><br>
	//! When dealing with industrial cameras, the camera intrinsic matrix or more precisely ![inline formula](https://latex.codecogs.com/png.latex?%20%5Cleft%28f%5Fx%2C%20f%5Fy%20%5Cright%29%20)
	//! can be deduced, approximated from the camera specifications:
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%0Af%5Fx%20%3D%20%5Cfrac%7Bf%5F%7B%5Ctext%7Bmm%7D%7D%7D%7B%5Ctext%7Bpixel%5Fsize%5Fin%5Fmm%7D%7D%20%3D%20%5Cfrac%7Bf%5F%7B%5Ctext%7Bmm%7D%7D%7D%7B%5Ctext%7Bsensor%5Fsize%5Fin%5Fmm%7D%20%2F%20%5Ctext%7Bnb%5Fpixels%7D%7D%0A)
	//!
	//! In a same way, the physical focal length can be deduced from the angular field of view:
	//!
	//! ![block formula](https://latex.codecogs.com/png.latex?%0Af%5F%7B%5Ctext%7Bmm%7D%7D%20%3D%20%5Cfrac%7B%5Ctext%7Bsensor%5Fsize%5Fin%5Fmm%7D%7D%7B2%20%5Ctimes%20%5Ctan%7B%5Cfrac%7B%5Ctext%7Bfov%7D%7D%7B2%7D%7D%7D%0A)
	//!
	//! This latter conversion can be useful when using a rendering software to mimic a physical camera device.
	//!
	//!
	//! Note:
	//!    *    See also [calibration_matrix_values]
	//!
	//! <B> Additional references, notes </B><br>
	//!
	//! Note:
	//!    *   Many functions in this module take a camera intrinsic matrix as an input parameter. Although all
	//!        functions assume the same structure of this parameter, they may name it differently. The
	//!        parameter's description, however, will be clear in that a camera intrinsic matrix with the structure
	//!        shown above is required.
	//!    *   A calibration sample for 3 cameras in a horizontal position can be found at
	//!        opencv_source_code/samples/cpp/3calibration.cpp
	//!    *   A calibration sample based on a sequence of images can be found at
	//!        opencv_source_code/samples/cpp/calibration.cpp
	//!    *   A calibration sample in order to do 3D reconstruction can be found at
	//!        opencv_source_code/samples/cpp/build3dmodel.cpp
	//!    *   A calibration example on stereo calibration can be found at
	//!        opencv_source_code/samples/cpp/stereo_calib.cpp
	//!    *   A calibration example on stereo matching can be found at
	//!        opencv_source_code/samples/cpp/stereo_match.cpp
	//!    *   (Python) A camera calibration sample can be found at
	//!        opencv_source_code/samples/python/calibrate.py
	//!    # Fisheye camera model
	//!
	//!    Definitions: Let P be a point in 3D of coordinates X in the world reference frame (stored in the
	//!    matrix X) The coordinate vector of P in the camera reference frame is:
	//!
	//!    ![block formula](https://latex.codecogs.com/png.latex?Xc%20%3D%20R%20X%20%2B%20T)
	//!
	//!    where R is the rotation matrix corresponding to the rotation vector om: R = rodrigues(om); call x, y
	//!    and z the 3 coordinates of Xc:
	//!
	//!    ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20x%20%3D%20Xc%5F1%20%5C%5C%20y%20%3D%20Xc%5F2%20%5C%5C%20z%20%3D%20Xc%5F3%20%5Cend%7Barray%7D%20)
	//!
	//!    The pinhole projection coordinates of P is [a; b] where
	//!
	//!    ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20a%20%3D%20x%20%2F%20z%20%5C%20and%20%5C%20b%20%3D%20y%20%2F%20z%20%5C%5C%20r%5E2%20%3D%20a%5E2%20%2B%20b%5E2%20%5C%5C%20%5Ctheta%20%3D%20atan%28r%29%20%5Cend%7Barray%7D%20)
	//!
	//!    Fisheye distortion:
	//!
	//!    ![block formula](https://latex.codecogs.com/png.latex?%5Ctheta%5Fd%20%3D%20%5Ctheta%20%281%20%2B%20k%5F1%20%5Ctheta%5E2%20%2B%20k%5F2%20%5Ctheta%5E4%20%2B%20k%5F3%20%5Ctheta%5E6%20%2B%20k%5F4%20%5Ctheta%5E8%29)
	//!
	//!    The distorted point coordinates are [x'; y'] where
	//!
	//!    ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20x%27%20%3D%20%28%5Ctheta%5Fd%20%2F%20r%29%20a%20%5C%5C%20y%27%20%3D%20%28%5Ctheta%5Fd%20%2F%20r%29%20b%20%5Cend%7Barray%7D%20)
	//!
	//!    Finally, conversion into pixel coordinates: The final pixel coordinates vector [u; v] where:
	//!
	//!    ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20u%20%3D%20f%5Fx%20%28x%27%20%2B%20%5Calpha%20y%27%29%20%2B%20c%5Fx%20%5C%5C%0A%20%20%20%20v%20%3D%20f%5Fy%20y%27%20%2B%20c%5Fy%20%5Cend%7Barray%7D%20)
	//!
	//!    Summary:
	//!    Generic camera model [Kannala2006](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Kannala2006) with perspective projection and without distortion correction
	use crate::mod_prelude::*;
	use crate::{core, sys, types};
	pub mod prelude {
		pub use super::{LMSolverTrait, LMSolverTraitConst, LMSolver_CallbackTrait, LMSolver_CallbackTraitConst, StereoBMTrait, StereoBMTraitConst, StereoMatcherTrait, StereoMatcherTraitConst, StereoSGBMTrait, StereoSGBMTraitConst};
	}

	pub const CALIB_CB_ACCURACY: i32 = 32;
	pub const CALIB_CB_ADAPTIVE_THRESH: i32 = 1;
	pub const CALIB_CB_ASYMMETRIC_GRID: i32 = 2;
	pub const CALIB_CB_CLUSTERING: i32 = 4;
	pub const CALIB_CB_EXHAUSTIVE: i32 = 16;
	pub const CALIB_CB_FAST_CHECK: i32 = 8;
	pub const CALIB_CB_FILTER_QUADS: i32 = 4;
	pub const CALIB_CB_LARGER: i32 = 64;
	pub const CALIB_CB_MARKER: i32 = 128;
	pub const CALIB_CB_NORMALIZE_IMAGE: i32 = 2;
	pub const CALIB_CB_PLAIN: i32 = 256;
	pub const CALIB_CB_SYMMETRIC_GRID: i32 = 1;
	pub const CALIB_FIX_ASPECT_RATIO: i32 = 2;
	pub const CALIB_FIX_FOCAL_LENGTH: i32 = 16;
	pub const CALIB_FIX_INTRINSIC: i32 = 256;
	pub const CALIB_FIX_K1: i32 = 32;
	pub const CALIB_FIX_K2: i32 = 64;
	pub const CALIB_FIX_K3: i32 = 128;
	pub const CALIB_FIX_K4: i32 = 2048;
	pub const CALIB_FIX_K5: i32 = 4096;
	pub const CALIB_FIX_K6: i32 = 8192;
	pub const CALIB_FIX_PRINCIPAL_POINT: i32 = 4;
	pub const CALIB_FIX_S1_S2_S3_S4: i32 = 65536;
	pub const CALIB_FIX_TANGENT_DIST: i32 = 2097152;
	pub const CALIB_FIX_TAUX_TAUY: i32 = 524288;
	/// On-line Hand-Eye Calibration [Andreff99](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Andreff99)
	pub const CALIB_HAND_EYE_ANDREFF: i32 = 3;
	/// Hand-Eye Calibration Using Dual Quaternions [Daniilidis98](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Daniilidis98)
	pub const CALIB_HAND_EYE_DANIILIDIS: i32 = 4;
	/// Hand-eye Calibration [Horaud95](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Horaud95)
	pub const CALIB_HAND_EYE_HORAUD: i32 = 2;
	/// Robot Sensor Calibration: Solving AX = XB on the Euclidean Group [Park94](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Park94)
	pub const CALIB_HAND_EYE_PARK: i32 = 1;
	/// A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/Eye Calibration [Tsai89](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Tsai89)
	pub const CALIB_HAND_EYE_TSAI: i32 = 0;
	pub const CALIB_NINTRINSIC: i32 = 18;
	pub const CALIB_RATIONAL_MODEL: i32 = 16384;
	/// Simultaneous robot-world and hand-eye calibration using dual-quaternions and kronecker product [Li2010SimultaneousRA](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Li2010SimultaneousRA)
	pub const CALIB_ROBOT_WORLD_HAND_EYE_LI: i32 = 1;
	/// Solving the robot-world/hand-eye calibration problem using the kronecker product [Shah2013SolvingTR](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Shah2013SolvingTR)
	pub const CALIB_ROBOT_WORLD_HAND_EYE_SHAH: i32 = 0;
	pub const CALIB_SAME_FOCAL_LENGTH: i32 = 512;
	pub const CALIB_THIN_PRISM_MODEL: i32 = 32768;
	pub const CALIB_TILTED_MODEL: i32 = 262144;
	/// for stereoCalibrate
	pub const CALIB_USE_EXTRINSIC_GUESS: i32 = 4194304;
	pub const CALIB_USE_INTRINSIC_GUESS: i32 = 1;
	/// use LU instead of SVD decomposition for solving. much faster but potentially less precise
	pub const CALIB_USE_LU: i32 = 131072;
	/// use QR instead of SVD decomposition for solving. Faster but potentially less precise
	pub const CALIB_USE_QR: i32 = 1048576;
	pub const CALIB_ZERO_DISPARITY: i32 = 1024;
	pub const CALIB_ZERO_TANGENT_DIST: i32 = 8;
	pub const COV_POLISHER: i32 = 3;
	pub const CirclesGridFinderParameters_ASYMMETRIC_GRID: i32 = 1;
	pub const CirclesGridFinderParameters_SYMMETRIC_GRID: i32 = 0;
	/// 7-point algorithm
	pub const FM_7POINT: i32 = 1;
	/// 8-point algorithm
	pub const FM_8POINT: i32 = 2;
	/// least-median algorithm. 7-point algorithm is used.
	pub const FM_LMEDS: i32 = 4;
	/// RANSAC algorithm. It needs at least 15 points. 7-point algorithm is used.
	pub const FM_RANSAC: i32 = 8;
	pub const Fisheye_CALIB_CHECK_COND: i32 = 4;
	pub const Fisheye_CALIB_FIX_FOCAL_LENGTH: i32 = 2048;
	pub const Fisheye_CALIB_FIX_INTRINSIC: i32 = 256;
	pub const Fisheye_CALIB_FIX_K1: i32 = 16;
	pub const Fisheye_CALIB_FIX_K2: i32 = 32;
	pub const Fisheye_CALIB_FIX_K3: i32 = 64;
	pub const Fisheye_CALIB_FIX_K4: i32 = 128;
	pub const Fisheye_CALIB_FIX_PRINCIPAL_POINT: i32 = 512;
	pub const Fisheye_CALIB_FIX_SKEW: i32 = 8;
	pub const Fisheye_CALIB_RECOMPUTE_EXTRINSIC: i32 = 2;
	pub const Fisheye_CALIB_USE_INTRINSIC_GUESS: i32 = 1;
	pub const Fisheye_CALIB_ZERO_DISPARITY: i32 = 1024;
	/// least-median of squares algorithm
	pub const LMEDS: i32 = 4;
	pub const LOCAL_OPTIM_GC: i32 = 3;
	pub const LOCAL_OPTIM_INNER_AND_ITER_LO: i32 = 2;
	pub const LOCAL_OPTIM_INNER_LO: i32 = 1;
	pub const LOCAL_OPTIM_NULL: i32 = 0;
	pub const LOCAL_OPTIM_SIGMA: i32 = 4;
	pub const LSQ_POLISHER: i32 = 1;
	pub const MAGSAC: i32 = 2;
	pub const NEIGH_FLANN_KNN: i32 = 0;
	pub const NEIGH_FLANN_RADIUS: i32 = 2;
	pub const NEIGH_GRID: i32 = 1;
	pub const NONE_POLISHER: i32 = 0;
	pub const PROJ_SPHERICAL_EQRECT: i32 = 1;
	pub const PROJ_SPHERICAL_ORTHO: i32 = 0;
	/// RANSAC algorithm
	pub const RANSAC: i32 = 8;
	/// RHO algorithm
	pub const RHO: i32 = 16;
	pub const SAMPLING_NAPSAC: i32 = 2;
	pub const SAMPLING_PROGRESSIVE_NAPSAC: i32 = 1;
	pub const SAMPLING_PROSAC: i32 = 3;
	pub const SAMPLING_UNIFORM: i32 = 0;
	pub const SCORE_METHOD_LMEDS: i32 = 3;
	pub const SCORE_METHOD_MAGSAC: i32 = 2;
	pub const SCORE_METHOD_MSAC: i32 = 1;
	pub const SCORE_METHOD_RANSAC: i32 = 0;
	/// An Efficient Algebraic Solution to the Perspective-Three-Point Problem [Ke17](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Ke17)
	pub const SOLVEPNP_AP3P: i32 = 5;
	/// **Broken implementation. Using this flag will fallback to EPnP.** 
	///
	/// A Direct Least-Squares (DLS) Method for PnP [hesch2011direct](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_hesch2011direct)
	pub const SOLVEPNP_DLS: i32 = 3;
	/// EPnP: Efficient Perspective-n-Point Camera Pose Estimation [lepetit2009epnp](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_lepetit2009epnp)
	pub const SOLVEPNP_EPNP: i32 = 1;
	/// Infinitesimal Plane-Based Pose Estimation [Collins14](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Collins14) 
	///
	/// Object points must be coplanar.
	pub const SOLVEPNP_IPPE: i32 = 6;
	/// Infinitesimal Plane-Based Pose Estimation [Collins14](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Collins14) 
	///
	/// This is a special case suitable for marker pose estimation.
	///
	/// 4 coplanar object points must be defined in the following order:
	///   - point 0: [-squareLength / 2,  squareLength / 2, 0]
	///   - point 1: [ squareLength / 2,  squareLength / 2, 0]
	///   - point 2: [ squareLength / 2, -squareLength / 2, 0]
	///   - point 3: [-squareLength / 2, -squareLength / 2, 0]
	pub const SOLVEPNP_IPPE_SQUARE: i32 = 7;
	/// Pose refinement using non-linear Levenberg-Marquardt minimization scheme [Madsen04](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Madsen04) [Eade13](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Eade13) 
	///
	/// Initial solution for non-planar "objectPoints" needs at least 6 points and uses the DLT algorithm. 
	///
	/// Initial solution for planar "objectPoints" needs at least 4 points and uses pose from homography decomposition.
	pub const SOLVEPNP_ITERATIVE: i32 = 0;
	/// Used for count
	pub const SOLVEPNP_MAX_COUNT: i32 = 9;
	/// Complete Solution Classification for the Perspective-Three-Point Problem [gao2003complete](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_gao2003complete)
	pub const SOLVEPNP_P3P: i32 = 2;
	/// SQPnP: A Consistently Fast and Globally OptimalSolution to the Perspective-n-Point Problem [Terzakis2020SQPnP](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Terzakis2020SQPnP)
	pub const SOLVEPNP_SQPNP: i32 = 8;
	/// **Broken implementation. Using this flag will fallback to EPnP.** 
	///
	/// Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation [penate2013exhaustive](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_penate2013exhaustive)
	pub const SOLVEPNP_UPNP: i32 = 4;
	/// Normalized response pre-filter
	pub const StereoBM_PREFILTER_NORMALIZED_RESPONSE: i32 = 0;
	/// X-Sobel pre-filter
	pub const StereoBM_PREFILTER_XSOBEL: i32 = 1;
	pub const StereoMatcher_DISP_SCALE: i32 = 16;
	pub const StereoMatcher_DISP_SHIFT: i32 = 4;
	pub const StereoSGBM_MODE_HH: i32 = 1;
	pub const StereoSGBM_MODE_HH4: i32 = 3;
	pub const StereoSGBM_MODE_SGBM: i32 = 0;
	pub const StereoSGBM_MODE_SGBM_3WAY: i32 = 2;
	/// USAC, accurate settings
	pub const USAC_ACCURATE: i32 = 36;
	/// USAC algorithm, default settings
	pub const USAC_DEFAULT: i32 = 32;
	/// USAC, fast settings
	pub const USAC_FAST: i32 = 35;
	/// USAC, fundamental matrix 8 points
	pub const USAC_FM_8PTS: i32 = 34;
	/// USAC, runs MAGSAC++
	pub const USAC_MAGSAC: i32 = 38;
	/// USAC, parallel version
	pub const USAC_PARALLEL: i32 = 33;
	/// USAC, sorted points, runs PROSAC
	pub const USAC_PROSAC: i32 = 37;
	#[repr(i32)]
	#[derive(Copy, Clone, Debug, PartialEq, Eq)]
	pub enum CirclesGridFinderParameters_GridType {
		SYMMETRIC_GRID = 0,
		ASYMMETRIC_GRID = 1,
	}

	opencv_type_enum! { crate::calib3d::CirclesGridFinderParameters_GridType { SYMMETRIC_GRID, ASYMMETRIC_GRID } }

	#[repr(i32)]
	#[derive(Copy, Clone, Debug, PartialEq, Eq)]
	pub enum HandEyeCalibrationMethod {
		/// A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/Eye Calibration [Tsai89](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Tsai89)
		CALIB_HAND_EYE_TSAI = 0,
		/// Robot Sensor Calibration: Solving AX = XB on the Euclidean Group [Park94](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Park94)
		CALIB_HAND_EYE_PARK = 1,
		/// Hand-eye Calibration [Horaud95](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Horaud95)
		CALIB_HAND_EYE_HORAUD = 2,
		/// On-line Hand-Eye Calibration [Andreff99](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Andreff99)
		CALIB_HAND_EYE_ANDREFF = 3,
		/// Hand-Eye Calibration Using Dual Quaternions [Daniilidis98](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Daniilidis98)
		CALIB_HAND_EYE_DANIILIDIS = 4,
	}

	opencv_type_enum! { crate::calib3d::HandEyeCalibrationMethod { CALIB_HAND_EYE_TSAI, CALIB_HAND_EYE_PARK, CALIB_HAND_EYE_HORAUD, CALIB_HAND_EYE_ANDREFF, CALIB_HAND_EYE_DANIILIDIS } }

	#[repr(i32)]
	#[derive(Copy, Clone, Debug, PartialEq, Eq)]
	pub enum LocalOptimMethod {
		LOCAL_OPTIM_NULL = 0,
		LOCAL_OPTIM_INNER_LO = 1,
		LOCAL_OPTIM_INNER_AND_ITER_LO = 2,
		LOCAL_OPTIM_GC = 3,
		LOCAL_OPTIM_SIGMA = 4,
	}

	opencv_type_enum! { crate::calib3d::LocalOptimMethod { LOCAL_OPTIM_NULL, LOCAL_OPTIM_INNER_LO, LOCAL_OPTIM_INNER_AND_ITER_LO, LOCAL_OPTIM_GC, LOCAL_OPTIM_SIGMA } }

	#[repr(i32)]
	#[derive(Copy, Clone, Debug, PartialEq, Eq)]
	pub enum NeighborSearchMethod {
		NEIGH_FLANN_KNN = 0,
		NEIGH_GRID = 1,
		NEIGH_FLANN_RADIUS = 2,
	}

	opencv_type_enum! { crate::calib3d::NeighborSearchMethod { NEIGH_FLANN_KNN, NEIGH_GRID, NEIGH_FLANN_RADIUS } }

	#[repr(i32)]
	#[derive(Copy, Clone, Debug, PartialEq, Eq)]
	pub enum PolishingMethod {
		NONE_POLISHER = 0,
		LSQ_POLISHER = 1,
		MAGSAC = 2,
		COV_POLISHER = 3,
	}

	opencv_type_enum! { crate::calib3d::PolishingMethod { NONE_POLISHER, LSQ_POLISHER, MAGSAC, COV_POLISHER } }

	#[repr(i32)]
	#[derive(Copy, Clone, Debug, PartialEq, Eq)]
	pub enum RobotWorldHandEyeCalibrationMethod {
		/// Solving the robot-world/hand-eye calibration problem using the kronecker product [Shah2013SolvingTR](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Shah2013SolvingTR)
		CALIB_ROBOT_WORLD_HAND_EYE_SHAH = 0,
		/// Simultaneous robot-world and hand-eye calibration using dual-quaternions and kronecker product [Li2010SimultaneousRA](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Li2010SimultaneousRA)
		CALIB_ROBOT_WORLD_HAND_EYE_LI = 1,
	}

	opencv_type_enum! { crate::calib3d::RobotWorldHandEyeCalibrationMethod { CALIB_ROBOT_WORLD_HAND_EYE_SHAH, CALIB_ROBOT_WORLD_HAND_EYE_LI } }

	#[repr(i32)]
	#[derive(Copy, Clone, Debug, PartialEq, Eq)]
	pub enum SamplingMethod {
		SAMPLING_UNIFORM = 0,
		SAMPLING_PROGRESSIVE_NAPSAC = 1,
		SAMPLING_NAPSAC = 2,
		SAMPLING_PROSAC = 3,
	}

	opencv_type_enum! { crate::calib3d::SamplingMethod { SAMPLING_UNIFORM, SAMPLING_PROGRESSIVE_NAPSAC, SAMPLING_NAPSAC, SAMPLING_PROSAC } }

	#[repr(i32)]
	#[derive(Copy, Clone, Debug, PartialEq, Eq)]
	pub enum ScoreMethod {
		SCORE_METHOD_RANSAC = 0,
		SCORE_METHOD_MSAC = 1,
		SCORE_METHOD_MAGSAC = 2,
		SCORE_METHOD_LMEDS = 3,
	}

	opencv_type_enum! { crate::calib3d::ScoreMethod { SCORE_METHOD_RANSAC, SCORE_METHOD_MSAC, SCORE_METHOD_MAGSAC, SCORE_METHOD_LMEDS } }

	#[repr(i32)]
	#[derive(Copy, Clone, Debug, PartialEq, Eq)]
	pub enum SolvePnPMethod {
		/// Pose refinement using non-linear Levenberg-Marquardt minimization scheme [Madsen04](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Madsen04) [Eade13](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Eade13) 
		///
		/// Initial solution for non-planar "objectPoints" needs at least 6 points and uses the DLT algorithm. 
		///
		/// Initial solution for planar "objectPoints" needs at least 4 points and uses pose from homography decomposition.
		SOLVEPNP_ITERATIVE = 0,
		/// EPnP: Efficient Perspective-n-Point Camera Pose Estimation [lepetit2009epnp](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_lepetit2009epnp)
		SOLVEPNP_EPNP = 1,
		/// Complete Solution Classification for the Perspective-Three-Point Problem [gao2003complete](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_gao2003complete)
		SOLVEPNP_P3P = 2,
		/// **Broken implementation. Using this flag will fallback to EPnP.** 
		///
		/// A Direct Least-Squares (DLS) Method for PnP [hesch2011direct](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_hesch2011direct)
		SOLVEPNP_DLS = 3,
		/// **Broken implementation. Using this flag will fallback to EPnP.** 
		///
		/// Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation [penate2013exhaustive](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_penate2013exhaustive)
		SOLVEPNP_UPNP = 4,
		/// An Efficient Algebraic Solution to the Perspective-Three-Point Problem [Ke17](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Ke17)
		SOLVEPNP_AP3P = 5,
		/// Infinitesimal Plane-Based Pose Estimation [Collins14](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Collins14) 
		///
		/// Object points must be coplanar.
		SOLVEPNP_IPPE = 6,
		/// Infinitesimal Plane-Based Pose Estimation [Collins14](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Collins14) 
		///
		/// This is a special case suitable for marker pose estimation.
		///
		/// 4 coplanar object points must be defined in the following order:
		///   - point 0: [-squareLength / 2,  squareLength / 2, 0]
		///   - point 1: [ squareLength / 2,  squareLength / 2, 0]
		///   - point 2: [ squareLength / 2, -squareLength / 2, 0]
		///   - point 3: [-squareLength / 2, -squareLength / 2, 0]
		SOLVEPNP_IPPE_SQUARE = 7,
		/// SQPnP: A Consistently Fast and Globally OptimalSolution to the Perspective-n-Point Problem [Terzakis2020SQPnP](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Terzakis2020SQPnP)
		SOLVEPNP_SQPNP = 8,
		/// Used for count
		SOLVEPNP_MAX_COUNT = 9,
	}

	opencv_type_enum! { crate::calib3d::SolvePnPMethod { SOLVEPNP_ITERATIVE, SOLVEPNP_EPNP, SOLVEPNP_P3P, SOLVEPNP_DLS, SOLVEPNP_UPNP, SOLVEPNP_AP3P, SOLVEPNP_IPPE, SOLVEPNP_IPPE_SQUARE, SOLVEPNP_SQPNP, SOLVEPNP_MAX_COUNT } }

	/// cv::undistort mode
	#[repr(i32)]
	#[derive(Copy, Clone, Debug, PartialEq, Eq)]
	pub enum UndistortTypes {
		PROJ_SPHERICAL_ORTHO = 0,
		PROJ_SPHERICAL_EQRECT = 1,
	}

	opencv_type_enum! { crate::calib3d::UndistortTypes { PROJ_SPHERICAL_ORTHO, PROJ_SPHERICAL_EQRECT } }

	pub type CirclesGridFinderParameters2 = crate::calib3d::CirclesGridFinderParameters;
	/// Computes an RQ decomposition of 3x3 matrices.
	///
	/// ## Parameters
	/// * src: 3x3 input matrix.
	/// * mtxR: Output 3x3 upper-triangular matrix.
	/// * mtxQ: Output 3x3 orthogonal matrix.
	/// * Qx: Optional output 3x3 rotation matrix around x-axis.
	/// * Qy: Optional output 3x3 rotation matrix around y-axis.
	/// * Qz: Optional output 3x3 rotation matrix around z-axis.
	///
	/// The function computes a RQ decomposition using the given rotations. This function is used in
	/// [decompose_projection_matrix] to decompose the left 3x3 submatrix of a projection matrix into a camera
	/// and a rotation matrix.
	///
	/// It optionally returns three rotation matrices, one for each axis, and the three Euler angles in
	/// degrees (as the return value) that could be used in OpenGL. Note, there is always more than one
	/// sequence of rotations about the three principal axes that results in the same orientation of an
	/// object, e.g. see [Slabaugh](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Slabaugh) . Returned three rotation matrices and corresponding three Euler angles
	/// are only one of the possible solutions.
	///
	/// ## Note
	/// This alternative version of [rq_decomp3x3] function uses the following default values for its arguments:
	/// * qx: noArray()
	/// * qy: noArray()
	/// * qz: noArray()
	#[inline]
	pub fn rq_decomp3x3_def(src: &impl ToInputArray, mtx_r: &mut impl ToOutputArray, mtx_q: &mut impl ToOutputArray) -> Result<core::Vec3d> {
		input_array_arg!(src);
		output_array_arg!(mtx_r);
		output_array_arg!(mtx_q);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_RQDecomp3x3_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(src.as_raw__InputArray(), mtx_r.as_raw__OutputArray(), mtx_q.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes an RQ decomposition of 3x3 matrices.
	///
	/// ## Parameters
	/// * src: 3x3 input matrix.
	/// * mtxR: Output 3x3 upper-triangular matrix.
	/// * mtxQ: Output 3x3 orthogonal matrix.
	/// * Qx: Optional output 3x3 rotation matrix around x-axis.
	/// * Qy: Optional output 3x3 rotation matrix around y-axis.
	/// * Qz: Optional output 3x3 rotation matrix around z-axis.
	///
	/// The function computes a RQ decomposition using the given rotations. This function is used in
	/// [decompose_projection_matrix] to decompose the left 3x3 submatrix of a projection matrix into a camera
	/// and a rotation matrix.
	///
	/// It optionally returns three rotation matrices, one for each axis, and the three Euler angles in
	/// degrees (as the return value) that could be used in OpenGL. Note, there is always more than one
	/// sequence of rotations about the three principal axes that results in the same orientation of an
	/// object, e.g. see [Slabaugh](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Slabaugh) . Returned three rotation matrices and corresponding three Euler angles
	/// are only one of the possible solutions.
	///
	/// ## C++ default parameters
	/// * qx: noArray()
	/// * qy: noArray()
	/// * qz: noArray()
	#[inline]
	pub fn rq_decomp3x3(src: &impl ToInputArray, mtx_r: &mut impl ToOutputArray, mtx_q: &mut impl ToOutputArray, qx: &mut impl ToOutputArray, qy: &mut impl ToOutputArray, qz: &mut impl ToOutputArray) -> Result<core::Vec3d> {
		input_array_arg!(src);
		output_array_arg!(mtx_r);
		output_array_arg!(mtx_q);
		output_array_arg!(qx);
		output_array_arg!(qy);
		output_array_arg!(qz);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_RQDecomp3x3_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(src.as_raw__InputArray(), mtx_r.as_raw__OutputArray(), mtx_q.as_raw__OutputArray(), qx.as_raw__OutputArray(), qy.as_raw__OutputArray(), qz.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Converts a rotation matrix to a rotation vector or vice versa.
	///
	/// ## Parameters
	/// * src: Input rotation vector (3x1 or 1x3) or rotation matrix (3x3).
	/// * dst: Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively.
	/// * jacobian: Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial
	/// derivatives of the output array components with respect to the input array components.
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20%5Ctheta%20%5Cleftarrow%20norm%28r%29%20%5C%5C%20r%20%20%5Cleftarrow%20r%2F%20%5Ctheta%20%5C%5C%20R%20%3D%20%20%5Ccos%28%5Ctheta%29%20I%20%2B%20%281%2D%20%5Ccos%7B%5Ctheta%7D%20%29%20r%20r%5ET%20%2B%20%20%5Csin%28%5Ctheta%29%20%5Cbegin%7Bbmatrix%7D%200%20%26%20%2Dr%5Fz%20%26%20r%5Fy%5C%5C%20r%5Fz%20%26%200%20%26%20%2Dr%5Fx%5C%5C%20%2Dr%5Fy%20%26%20r%5Fx%20%26%200%20%5Cend%7Bbmatrix%7D%20%5Cend%7Barray%7D)
	///
	/// Inverse transformation can be also done easily, since
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Csin%20%28%20%5Ctheta%20%29%20%5Cbegin%7Bbmatrix%7D%200%20%26%20%2Dr%5Fz%20%26%20r%5Fy%5C%5C%20r%5Fz%20%26%200%20%26%20%2Dr%5Fx%5C%5C%20%2Dr%5Fy%20%26%20r%5Fx%20%26%200%20%5Cend%7Bbmatrix%7D%20%3D%20%5Cfrac%7BR%20%2D%20R%5ET%7D%7B2%7D)
	///
	/// A rotation vector is a convenient and most compact representation of a rotation matrix (since any
	/// rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry
	/// optimization procedures like [calibrateCamera], [stereoCalibrate], or [solvePnP] .
	///
	///
	/// Note: More information about the computation of the derivative of a 3D rotation matrix with respect to its exponential coordinate
	/// can be found in:
	///    - A Compact Formula for the Derivative of a 3-D Rotation in Exponential Coordinates, Guillermo Gallego, Anthony J. Yezzi [Gallego2014ACF](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Gallego2014ACF)
	///
	///
	/// Note: Useful information on SE(3) and Lie Groups can be found in:
	///    - A tutorial on SE(3) transformation parameterizations and on-manifold optimization, Jose-Luis Blanco [blanco2010tutorial](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_blanco2010tutorial)
	///    - Lie Groups for 2D and 3D Transformation, Ethan Eade [Eade17](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Eade17)
	///    - A micro Lie theory for state estimation in robotics, Joan Solà, Jérémie Deray, Dinesh Atchuthan [Sol2018AML](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Sol2018AML)
	///
	/// ## Note
	/// This alternative version of [rodrigues] function uses the following default values for its arguments:
	/// * jacobian: noArray()
	#[inline]
	pub fn rodrigues_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(src);
		output_array_arg!(dst);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_Rodrigues_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Converts a rotation matrix to a rotation vector or vice versa.
	///
	/// ## Parameters
	/// * src: Input rotation vector (3x1 or 1x3) or rotation matrix (3x3).
	/// * dst: Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively.
	/// * jacobian: Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial
	/// derivatives of the output array components with respect to the input array components.
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20%5Ctheta%20%5Cleftarrow%20norm%28r%29%20%5C%5C%20r%20%20%5Cleftarrow%20r%2F%20%5Ctheta%20%5C%5C%20R%20%3D%20%20%5Ccos%28%5Ctheta%29%20I%20%2B%20%281%2D%20%5Ccos%7B%5Ctheta%7D%20%29%20r%20r%5ET%20%2B%20%20%5Csin%28%5Ctheta%29%20%5Cbegin%7Bbmatrix%7D%200%20%26%20%2Dr%5Fz%20%26%20r%5Fy%5C%5C%20r%5Fz%20%26%200%20%26%20%2Dr%5Fx%5C%5C%20%2Dr%5Fy%20%26%20r%5Fx%20%26%200%20%5Cend%7Bbmatrix%7D%20%5Cend%7Barray%7D)
	///
	/// Inverse transformation can be also done easily, since
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Csin%20%28%20%5Ctheta%20%29%20%5Cbegin%7Bbmatrix%7D%200%20%26%20%2Dr%5Fz%20%26%20r%5Fy%5C%5C%20r%5Fz%20%26%200%20%26%20%2Dr%5Fx%5C%5C%20%2Dr%5Fy%20%26%20r%5Fx%20%26%200%20%5Cend%7Bbmatrix%7D%20%3D%20%5Cfrac%7BR%20%2D%20R%5ET%7D%7B2%7D)
	///
	/// A rotation vector is a convenient and most compact representation of a rotation matrix (since any
	/// rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry
	/// optimization procedures like [calibrateCamera], [stereoCalibrate], or [solvePnP] .
	///
	///
	/// Note: More information about the computation of the derivative of a 3D rotation matrix with respect to its exponential coordinate
	/// can be found in:
	///    - A Compact Formula for the Derivative of a 3-D Rotation in Exponential Coordinates, Guillermo Gallego, Anthony J. Yezzi [Gallego2014ACF](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Gallego2014ACF)
	///
	///
	/// Note: Useful information on SE(3) and Lie Groups can be found in:
	///    - A tutorial on SE(3) transformation parameterizations and on-manifold optimization, Jose-Luis Blanco [blanco2010tutorial](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_blanco2010tutorial)
	///    - Lie Groups for 2D and 3D Transformation, Ethan Eade [Eade17](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Eade17)
	///    - A micro Lie theory for state estimation in robotics, Joan Solà, Jérémie Deray, Dinesh Atchuthan [Sol2018AML](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Sol2018AML)
	///
	/// ## C++ default parameters
	/// * jacobian: noArray()
	#[inline]
	pub fn rodrigues(src: &impl ToInputArray, dst: &mut impl ToOutputArray, jacobian: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(src);
		output_array_arg!(dst);
		output_array_arg!(jacobian);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_Rodrigues_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), jacobian.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
	///
	/// This function is an extension of [calibrate_camera] with the method of releasing object which was
	/// proposed in [strobl2011iccv](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_strobl2011iccv). In many common cases with inaccurate, unmeasured, roughly planar
	/// targets (calibration plates), this method can dramatically improve the precision of the estimated
	/// camera parameters. Both the object-releasing method and standard method are supported by this
	/// function. Use the parameter **iFixedPoint** for method selection. In the internal implementation,
	/// [calibrate_camera] is a wrapper for this function.
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of calibration pattern points in the calibration pattern
	/// coordinate space. See [calibrate_camera] for details. If the method of releasing object to be used,
	/// the identical calibration board must be used in each view and it must be fully visible, and all
	/// objectPoints[i] must be the same and all points should be roughly close to a plane. **The calibration
	/// target has to be rigid, or at least static if the camera (rather than the calibration target) is
	/// shifted for grabbing images.**
	/// * imagePoints: Vector of vectors of the projections of calibration pattern points. See
	/// [calibrate_camera] for details.
	/// * imageSize: Size of the image used only to initialize the intrinsic camera matrix.
	/// * iFixedPoint: The index of the 3D object point in objectPoints[0] to be fixed. It also acts as
	/// a switch for calibration method selection. If object-releasing method to be used, pass in the
	/// parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will
	/// make standard calibration method selected. Usually the top-right corner point of the calibration
	/// board grid is recommended to be fixed when object-releasing method being utilized. According to
	/// \cite strobl2011iccv, two other points are also fixed. In this implementation, objectPoints[0].front
	/// and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and
	/// newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
	/// * cameraMatrix: Output 3x3 floating-point camera matrix. See [calibrate_camera] for details.
	/// * distCoeffs: Output vector of distortion coefficients. See [calibrate_camera] for details.
	/// * rvecs: Output vector of rotation vectors estimated for each pattern view. See [calibrate_camera]
	/// for details.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view.
	/// * newObjPoints: The updated output vector of calibration pattern points. The coordinates might
	/// be scaled based on three fixed points. The returned coordinates are accurate only if the above
	/// mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter
	/// is ignored with standard calibration method.
	/// * stdDeviationsIntrinsics: Output vector of standard deviations estimated for intrinsic parameters.
	/// See [calibrate_camera] for details.
	/// * stdDeviationsExtrinsics: Output vector of standard deviations estimated for extrinsic parameters.
	/// See [calibrate_camera] for details.
	/// * stdDeviationsObjPoints: Output vector of standard deviations estimated for refined coordinates
	/// of calibration pattern points. It has the same size and order as objectPoints[0] vector. This
	/// parameter is ignored with standard calibration method.
	/// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of some predefined values. See
	/// [calibrate_camera] for details. If the method of releasing object is used, the calibration time may
	/// be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially
	/// less precise and less stable in some rare cases.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// ## Returns
	/// the overall RMS re-projection error.
	///
	/// The function estimates the intrinsic camera parameters and extrinsic parameters for each of the
	/// views. The algorithm is based on [Zhang2000](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Zhang2000), [BouguetMCT](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_BouguetMCT) and [strobl2011iccv](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_strobl2011iccv). See
	/// [calibrate_camera] for other detailed explanations.
	/// ## See also
	/// calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort
	///
	/// ## Overloaded parameters
	///
	///
	/// ## Note
	/// This alternative version of [calibrate_camera_ro] function uses the following default values for its arguments:
	/// * flags: 0
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,30,DBL_EPSILON)
	#[inline]
	pub fn calibrate_camera_ro_def(object_points: &impl ToInputArray, image_points: &impl ToInputArray, image_size: core::Size, i_fixed_point: i32, camera_matrix: &mut impl ToInputOutputArray, dist_coeffs: &mut impl ToInputOutputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray, new_obj_points: &mut impl ToOutputArray) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_output_array_arg!(camera_matrix);
		input_output_array_arg!(dist_coeffs);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		output_array_arg!(new_obj_points);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_calibrateCameraRO_const__InputArrayR_const__InputArrayR_Size_int_const__InputOutputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), &image_size, i_fixed_point, camera_matrix.as_raw__InputOutputArray(), dist_coeffs.as_raw__InputOutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), new_obj_points.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
	///
	/// This function is an extension of [calibrate_camera] with the method of releasing object which was
	/// proposed in [strobl2011iccv](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_strobl2011iccv). In many common cases with inaccurate, unmeasured, roughly planar
	/// targets (calibration plates), this method can dramatically improve the precision of the estimated
	/// camera parameters. Both the object-releasing method and standard method are supported by this
	/// function. Use the parameter **iFixedPoint** for method selection. In the internal implementation,
	/// [calibrate_camera] is a wrapper for this function.
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of calibration pattern points in the calibration pattern
	/// coordinate space. See [calibrate_camera] for details. If the method of releasing object to be used,
	/// the identical calibration board must be used in each view and it must be fully visible, and all
	/// objectPoints[i] must be the same and all points should be roughly close to a plane. **The calibration
	/// target has to be rigid, or at least static if the camera (rather than the calibration target) is
	/// shifted for grabbing images.**
	/// * imagePoints: Vector of vectors of the projections of calibration pattern points. See
	/// [calibrate_camera] for details.
	/// * imageSize: Size of the image used only to initialize the intrinsic camera matrix.
	/// * iFixedPoint: The index of the 3D object point in objectPoints[0] to be fixed. It also acts as
	/// a switch for calibration method selection. If object-releasing method to be used, pass in the
	/// parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will
	/// make standard calibration method selected. Usually the top-right corner point of the calibration
	/// board grid is recommended to be fixed when object-releasing method being utilized. According to
	/// \cite strobl2011iccv, two other points are also fixed. In this implementation, objectPoints[0].front
	/// and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and
	/// newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
	/// * cameraMatrix: Output 3x3 floating-point camera matrix. See [calibrate_camera] for details.
	/// * distCoeffs: Output vector of distortion coefficients. See [calibrate_camera] for details.
	/// * rvecs: Output vector of rotation vectors estimated for each pattern view. See [calibrate_camera]
	/// for details.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view.
	/// * newObjPoints: The updated output vector of calibration pattern points. The coordinates might
	/// be scaled based on three fixed points. The returned coordinates are accurate only if the above
	/// mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter
	/// is ignored with standard calibration method.
	/// * stdDeviationsIntrinsics: Output vector of standard deviations estimated for intrinsic parameters.
	/// See [calibrate_camera] for details.
	/// * stdDeviationsExtrinsics: Output vector of standard deviations estimated for extrinsic parameters.
	/// See [calibrate_camera] for details.
	/// * stdDeviationsObjPoints: Output vector of standard deviations estimated for refined coordinates
	/// of calibration pattern points. It has the same size and order as objectPoints[0] vector. This
	/// parameter is ignored with standard calibration method.
	/// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of some predefined values. See
	/// [calibrate_camera] for details. If the method of releasing object is used, the calibration time may
	/// be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially
	/// less precise and less stable in some rare cases.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// ## Returns
	/// the overall RMS re-projection error.
	///
	/// The function estimates the intrinsic camera parameters and extrinsic parameters for each of the
	/// views. The algorithm is based on [Zhang2000](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Zhang2000), [BouguetMCT](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_BouguetMCT) and [strobl2011iccv](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_strobl2011iccv). See
	/// [calibrate_camera] for other detailed explanations.
	/// ## See also
	/// calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort
	///
	/// ## Note
	/// This alternative version of [calibrate_camera_ro_extended] function uses the following default values for its arguments:
	/// * flags: 0
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,30,DBL_EPSILON)
	#[inline]
	pub fn calibrate_camera_ro_extended_def(object_points: &impl ToInputArray, image_points: &impl ToInputArray, image_size: core::Size, i_fixed_point: i32, camera_matrix: &mut impl ToInputOutputArray, dist_coeffs: &mut impl ToInputOutputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray, new_obj_points: &mut impl ToOutputArray, std_deviations_intrinsics: &mut impl ToOutputArray, std_deviations_extrinsics: &mut impl ToOutputArray, std_deviations_obj_points: &mut impl ToOutputArray, per_view_errors: &mut impl ToOutputArray) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_output_array_arg!(camera_matrix);
		input_output_array_arg!(dist_coeffs);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		output_array_arg!(new_obj_points);
		output_array_arg!(std_deviations_intrinsics);
		output_array_arg!(std_deviations_extrinsics);
		output_array_arg!(std_deviations_obj_points);
		output_array_arg!(per_view_errors);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_calibrateCameraRO_const__InputArrayR_const__InputArrayR_Size_int_const__InputOutputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), &image_size, i_fixed_point, camera_matrix.as_raw__InputOutputArray(), dist_coeffs.as_raw__InputOutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), new_obj_points.as_raw__OutputArray(), std_deviations_intrinsics.as_raw__OutputArray(), std_deviations_extrinsics.as_raw__OutputArray(), std_deviations_obj_points.as_raw__OutputArray(), per_view_errors.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
	///
	/// This function is an extension of [calibrate_camera] with the method of releasing object which was
	/// proposed in [strobl2011iccv](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_strobl2011iccv). In many common cases with inaccurate, unmeasured, roughly planar
	/// targets (calibration plates), this method can dramatically improve the precision of the estimated
	/// camera parameters. Both the object-releasing method and standard method are supported by this
	/// function. Use the parameter **iFixedPoint** for method selection. In the internal implementation,
	/// [calibrate_camera] is a wrapper for this function.
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of calibration pattern points in the calibration pattern
	/// coordinate space. See [calibrate_camera] for details. If the method of releasing object to be used,
	/// the identical calibration board must be used in each view and it must be fully visible, and all
	/// objectPoints[i] must be the same and all points should be roughly close to a plane. **The calibration
	/// target has to be rigid, or at least static if the camera (rather than the calibration target) is
	/// shifted for grabbing images.**
	/// * imagePoints: Vector of vectors of the projections of calibration pattern points. See
	/// [calibrate_camera] for details.
	/// * imageSize: Size of the image used only to initialize the intrinsic camera matrix.
	/// * iFixedPoint: The index of the 3D object point in objectPoints[0] to be fixed. It also acts as
	/// a switch for calibration method selection. If object-releasing method to be used, pass in the
	/// parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will
	/// make standard calibration method selected. Usually the top-right corner point of the calibration
	/// board grid is recommended to be fixed when object-releasing method being utilized. According to
	/// \cite strobl2011iccv, two other points are also fixed. In this implementation, objectPoints[0].front
	/// and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and
	/// newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
	/// * cameraMatrix: Output 3x3 floating-point camera matrix. See [calibrate_camera] for details.
	/// * distCoeffs: Output vector of distortion coefficients. See [calibrate_camera] for details.
	/// * rvecs: Output vector of rotation vectors estimated for each pattern view. See [calibrate_camera]
	/// for details.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view.
	/// * newObjPoints: The updated output vector of calibration pattern points. The coordinates might
	/// be scaled based on three fixed points. The returned coordinates are accurate only if the above
	/// mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter
	/// is ignored with standard calibration method.
	/// * stdDeviationsIntrinsics: Output vector of standard deviations estimated for intrinsic parameters.
	/// See [calibrate_camera] for details.
	/// * stdDeviationsExtrinsics: Output vector of standard deviations estimated for extrinsic parameters.
	/// See [calibrate_camera] for details.
	/// * stdDeviationsObjPoints: Output vector of standard deviations estimated for refined coordinates
	/// of calibration pattern points. It has the same size and order as objectPoints[0] vector. This
	/// parameter is ignored with standard calibration method.
	/// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of some predefined values. See
	/// [calibrate_camera] for details. If the method of releasing object is used, the calibration time may
	/// be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially
	/// less precise and less stable in some rare cases.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// ## Returns
	/// the overall RMS re-projection error.
	///
	/// The function estimates the intrinsic camera parameters and extrinsic parameters for each of the
	/// views. The algorithm is based on [Zhang2000](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Zhang2000), [BouguetMCT](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_BouguetMCT) and [strobl2011iccv](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_strobl2011iccv). See
	/// [calibrate_camera] for other detailed explanations.
	/// ## See also
	/// calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort
	///
	/// ## C++ default parameters
	/// * flags: 0
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,30,DBL_EPSILON)
	#[inline]
	pub fn calibrate_camera_ro_extended(object_points: &impl ToInputArray, image_points: &impl ToInputArray, image_size: core::Size, i_fixed_point: i32, camera_matrix: &mut impl ToInputOutputArray, dist_coeffs: &mut impl ToInputOutputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray, new_obj_points: &mut impl ToOutputArray, std_deviations_intrinsics: &mut impl ToOutputArray, std_deviations_extrinsics: &mut impl ToOutputArray, std_deviations_obj_points: &mut impl ToOutputArray, per_view_errors: &mut impl ToOutputArray, flags: i32, criteria: core::TermCriteria) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_output_array_arg!(camera_matrix);
		input_output_array_arg!(dist_coeffs);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		output_array_arg!(new_obj_points);
		output_array_arg!(std_deviations_intrinsics);
		output_array_arg!(std_deviations_extrinsics);
		output_array_arg!(std_deviations_obj_points);
		output_array_arg!(per_view_errors);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_calibrateCameraRO_const__InputArrayR_const__InputArrayR_Size_int_const__InputOutputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_TermCriteria(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), &image_size, i_fixed_point, camera_matrix.as_raw__InputOutputArray(), dist_coeffs.as_raw__InputOutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), new_obj_points.as_raw__OutputArray(), std_deviations_intrinsics.as_raw__OutputArray(), std_deviations_extrinsics.as_raw__OutputArray(), std_deviations_obj_points.as_raw__OutputArray(), per_view_errors.as_raw__OutputArray(), flags, &criteria, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
	///
	/// This function is an extension of [calibrate_camera] with the method of releasing object which was
	/// proposed in [strobl2011iccv](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_strobl2011iccv). In many common cases with inaccurate, unmeasured, roughly planar
	/// targets (calibration plates), this method can dramatically improve the precision of the estimated
	/// camera parameters. Both the object-releasing method and standard method are supported by this
	/// function. Use the parameter **iFixedPoint** for method selection. In the internal implementation,
	/// [calibrate_camera] is a wrapper for this function.
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of calibration pattern points in the calibration pattern
	/// coordinate space. See [calibrate_camera] for details. If the method of releasing object to be used,
	/// the identical calibration board must be used in each view and it must be fully visible, and all
	/// objectPoints[i] must be the same and all points should be roughly close to a plane. **The calibration
	/// target has to be rigid, or at least static if the camera (rather than the calibration target) is
	/// shifted for grabbing images.**
	/// * imagePoints: Vector of vectors of the projections of calibration pattern points. See
	/// [calibrate_camera] for details.
	/// * imageSize: Size of the image used only to initialize the intrinsic camera matrix.
	/// * iFixedPoint: The index of the 3D object point in objectPoints[0] to be fixed. It also acts as
	/// a switch for calibration method selection. If object-releasing method to be used, pass in the
	/// parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will
	/// make standard calibration method selected. Usually the top-right corner point of the calibration
	/// board grid is recommended to be fixed when object-releasing method being utilized. According to
	/// \cite strobl2011iccv, two other points are also fixed. In this implementation, objectPoints[0].front
	/// and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and
	/// newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
	/// * cameraMatrix: Output 3x3 floating-point camera matrix. See [calibrate_camera] for details.
	/// * distCoeffs: Output vector of distortion coefficients. See [calibrate_camera] for details.
	/// * rvecs: Output vector of rotation vectors estimated for each pattern view. See [calibrate_camera]
	/// for details.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view.
	/// * newObjPoints: The updated output vector of calibration pattern points. The coordinates might
	/// be scaled based on three fixed points. The returned coordinates are accurate only if the above
	/// mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter
	/// is ignored with standard calibration method.
	/// * stdDeviationsIntrinsics: Output vector of standard deviations estimated for intrinsic parameters.
	/// See [calibrate_camera] for details.
	/// * stdDeviationsExtrinsics: Output vector of standard deviations estimated for extrinsic parameters.
	/// See [calibrate_camera] for details.
	/// * stdDeviationsObjPoints: Output vector of standard deviations estimated for refined coordinates
	/// of calibration pattern points. It has the same size and order as objectPoints[0] vector. This
	/// parameter is ignored with standard calibration method.
	/// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of some predefined values. See
	/// [calibrate_camera] for details. If the method of releasing object is used, the calibration time may
	/// be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially
	/// less precise and less stable in some rare cases.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// ## Returns
	/// the overall RMS re-projection error.
	///
	/// The function estimates the intrinsic camera parameters and extrinsic parameters for each of the
	/// views. The algorithm is based on [Zhang2000](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Zhang2000), [BouguetMCT](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_BouguetMCT) and [strobl2011iccv](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_strobl2011iccv). See
	/// [calibrate_camera] for other detailed explanations.
	/// ## See also
	/// calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort
	///
	/// ## Overloaded parameters
	///
	/// ## C++ default parameters
	/// * flags: 0
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,30,DBL_EPSILON)
	#[inline]
	pub fn calibrate_camera_ro(object_points: &impl ToInputArray, image_points: &impl ToInputArray, image_size: core::Size, i_fixed_point: i32, camera_matrix: &mut impl ToInputOutputArray, dist_coeffs: &mut impl ToInputOutputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray, new_obj_points: &mut impl ToOutputArray, flags: i32, criteria: core::TermCriteria) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_output_array_arg!(camera_matrix);
		input_output_array_arg!(dist_coeffs);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		output_array_arg!(new_obj_points);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_calibrateCameraRO_const__InputArrayR_const__InputArrayR_Size_int_const__InputOutputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_TermCriteria(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), &image_size, i_fixed_point, camera_matrix.as_raw__InputOutputArray(), dist_coeffs.as_raw__InputOutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), new_obj_points.as_raw__OutputArray(), flags, &criteria, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds the camera intrinsic and extrinsic parameters from several views of a calibration
	/// pattern.
	///
	/// ## Parameters
	/// * objectPoints: In the new interface it is a vector of vectors of calibration pattern points in
	/// the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer
	/// vector contains as many elements as the number of pattern views. If the same calibration pattern
	/// is shown in each view and it is fully visible, all the vectors will be the same. Although, it is
	/// possible to use partially occluded patterns or even different patterns in different views. Then,
	/// the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's
	/// XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig.
	/// In the old interface all the vectors of object points from different views are concatenated
	/// together.
	/// * imagePoints: In the new interface it is a vector of vectors of the projections of calibration
	/// pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and
	/// objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal,
	/// respectively. In the old interface all the vectors of object points from different views are
	/// concatenated together.
	/// * imageSize: Size of the image used only to initialize the camera intrinsic matrix.
	/// * cameraMatrix: Input/output 3x3 floating-point camera intrinsic matrix
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) . If [CALIB_USE_INTRINSIC_GUESS]
	/// and/or [CALIB_FIX_ASPECT_RATIO], [CALIB_FIX_PRINCIPAL_POINT] or [CALIB_FIX_FOCAL_LENGTH]
	/// are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
	/// * distCoeffs: Input/output vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs).
	/// * rvecs: Output vector of rotation vectors ([Rodrigues] ) estimated for each pattern view
	/// (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding
	/// i-th translation vector (see the next output parameter description) brings the calibration pattern
	/// from the object coordinate space (in which object points are specified) to the camera coordinate
	/// space. In more technical terms, the tuple of the i-th rotation and translation vector performs
	/// a change of basis from object coordinate space to camera coordinate space. Due to its duality, this
	/// tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate
	/// space.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view, see parameter
	/// describtion above.
	/// * stdDeviationsIntrinsics: Output vector of standard deviations estimated for intrinsic
	/// parameters. Order of deviations values:
	/// ![inline formula](https://latex.codecogs.com/png.latex?%28f%5Fx%2C%20f%5Fy%2C%20c%5Fx%2C%20c%5Fy%2C%20k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%2C%20k%5F3%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%20%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%0A%20s%5F4%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%29) If one of parameters is not estimated, it's deviation is equals to zero.
	/// * stdDeviationsExtrinsics: Output vector of standard deviations estimated for extrinsic
	/// parameters. Order of deviations values: ![inline formula](https://latex.codecogs.com/png.latex?%28R%5F0%2C%20T%5F0%2C%20%5Cdotsc%20%2C%20R%5F%7BM%20%2D%201%7D%2C%20T%5F%7BM%20%2D%201%7D%29) where M is
	/// the number of pattern views. ![inline formula](https://latex.codecogs.com/png.latex?R%5Fi%2C%20T%5Fi) are concatenated 1x3 vectors.
	/// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of the following values:
	/// *   [CALIB_USE_INTRINSIC_GUESS] cameraMatrix contains valid initial values of
	/// fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
	/// center ( imageSize is used), and focal distances are computed in a least-squares fashion.
	/// Note, that if intrinsic parameters are known, there is no need to use this function just to
	/// estimate extrinsic parameters. Use [solvePnP] instead.
	/// *   [CALIB_FIX_PRINCIPAL_POINT] The principal point is not changed during the global
	/// optimization. It stays at the center or at a different location specified when
	///  [CALIB_USE_INTRINSIC_GUESS] is set too.
	/// *   [CALIB_FIX_ASPECT_RATIO] The functions consider only fy as a free parameter. The
	/// ratio fx/fy stays the same as in the input cameraMatrix . When
	///  [CALIB_USE_INTRINSIC_GUESS] is not set, the actual input values of fx and fy are
	/// ignored, only their ratio is computed and used further.
	/// *   [CALIB_ZERO_TANGENT_DIST] Tangential distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%28p%5F1%2C%20p%5F2%29) are set
	/// to zeros and stay zero.
	/// *   [CALIB_FIX_FOCAL_LENGTH] The focal length is not changed during the global optimization if
	///  [CALIB_USE_INTRINSIC_GUESS] is set.
	/// *   [CALIB_FIX_K1],..., [CALIB_FIX_K6] The corresponding radial distortion
	/// coefficient is not changed during the optimization. If [CALIB_USE_INTRINSIC_GUESS] is
	/// set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_RATIONAL_MODEL] Coefficients k4, k5, and k6 are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the rational model and return 8 coefficients or more.
	/// *   [CALIB_THIN_PRISM_MODEL] Coefficients s1, s2, s3 and s4 are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the thin prism model and return 12 coefficients or more.
	/// *   [CALIB_FIX_S1_S2_S3_S4] The thin prism distortion coefficients are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_TILTED_MODEL] Coefficients tauX and tauY are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the tilted sensor model and return 14 coefficients.
	/// *   [CALIB_FIX_TAUX_TAUY] The coefficients of the tilted sensor model are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// ## Returns
	/// the overall RMS re-projection error.
	///
	/// The function estimates the intrinsic camera parameters and extrinsic parameters for each of the
	/// views. The algorithm is based on [Zhang2000](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Zhang2000) and [BouguetMCT](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_BouguetMCT) . The coordinates of 3D object
	/// points and their corresponding 2D projections in each view must be specified. That may be achieved
	/// by using an object with known geometry and easily detectable feature points. Such an object is
	/// called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as
	/// a calibration rig (see [findChessboardCorners]). Currently, initialization of intrinsic
	/// parameters (when [CALIB_USE_INTRINSIC_GUESS] is not set) is only implemented for planar calibration
	/// patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also
	/// be used as long as initial cameraMatrix is provided.
	///
	/// The algorithm performs the following steps:
	///
	/// *   Compute the initial intrinsic parameters (the option only available for planar calibration
	///    patterns) or read them from the input parameters. The distortion coefficients are all set to
	///    zeros initially unless some of CALIB_FIX_K? are specified.
	///
	/// *   Estimate the initial camera pose as if the intrinsic parameters have been already known. This is
	///    done using [solvePnP] .
	///
	/// *   Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error,
	///    that is, the total sum of squared distances between the observed feature points imagePoints and
	///    the projected (using the current estimates for camera parameters and the poses) object points
	///    objectPoints. See [projectPoints] for details.
	///
	///
	/// Note:
	///    If you use a non-square (i.e. non-N-by-N) grid and [findChessboardCorners] for calibration,
	///    and [calibrateCamera] returns bad values (zero distortion coefficients, ![inline formula](https://latex.codecogs.com/png.latex?c%5Fx) and
	///    ![inline formula](https://latex.codecogs.com/png.latex?c%5Fy) very far from the image center, and/or large differences between ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx) and
	///    ![inline formula](https://latex.codecogs.com/png.latex?f%5Fy) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols)
	///    instead of using patternSize=cvSize(cols,rows) in [findChessboardCorners].
	///
	///
	/// Note:
	///    The function may throw exceptions, if unsupported combination of parameters is provided or
	///    the system is underconstrained.
	/// ## See also
	/// calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate,
	///    undistort
	///
	/// ## Overloaded parameters
	///
	///
	/// ## Note
	/// This alternative version of [calibrate_camera] function uses the following default values for its arguments:
	/// * flags: 0
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,30,DBL_EPSILON)
	#[inline]
	pub fn calibrate_camera_def(object_points: &impl ToInputArray, image_points: &impl ToInputArray, image_size: core::Size, camera_matrix: &mut impl ToInputOutputArray, dist_coeffs: &mut impl ToInputOutputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_output_array_arg!(camera_matrix);
		input_output_array_arg!(dist_coeffs);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_calibrateCamera_const__InputArrayR_const__InputArrayR_Size_const__InputOutputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), &image_size, camera_matrix.as_raw__InputOutputArray(), dist_coeffs.as_raw__InputOutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds the camera intrinsic and extrinsic parameters from several views of a calibration
	/// pattern.
	///
	/// ## Parameters
	/// * objectPoints: In the new interface it is a vector of vectors of calibration pattern points in
	/// the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer
	/// vector contains as many elements as the number of pattern views. If the same calibration pattern
	/// is shown in each view and it is fully visible, all the vectors will be the same. Although, it is
	/// possible to use partially occluded patterns or even different patterns in different views. Then,
	/// the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's
	/// XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig.
	/// In the old interface all the vectors of object points from different views are concatenated
	/// together.
	/// * imagePoints: In the new interface it is a vector of vectors of the projections of calibration
	/// pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and
	/// objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal,
	/// respectively. In the old interface all the vectors of object points from different views are
	/// concatenated together.
	/// * imageSize: Size of the image used only to initialize the camera intrinsic matrix.
	/// * cameraMatrix: Input/output 3x3 floating-point camera intrinsic matrix
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) . If [CALIB_USE_INTRINSIC_GUESS]
	/// and/or [CALIB_FIX_ASPECT_RATIO], [CALIB_FIX_PRINCIPAL_POINT] or [CALIB_FIX_FOCAL_LENGTH]
	/// are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
	/// * distCoeffs: Input/output vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs).
	/// * rvecs: Output vector of rotation vectors ([Rodrigues] ) estimated for each pattern view
	/// (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding
	/// i-th translation vector (see the next output parameter description) brings the calibration pattern
	/// from the object coordinate space (in which object points are specified) to the camera coordinate
	/// space. In more technical terms, the tuple of the i-th rotation and translation vector performs
	/// a change of basis from object coordinate space to camera coordinate space. Due to its duality, this
	/// tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate
	/// space.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view, see parameter
	/// describtion above.
	/// * stdDeviationsIntrinsics: Output vector of standard deviations estimated for intrinsic
	/// parameters. Order of deviations values:
	/// ![inline formula](https://latex.codecogs.com/png.latex?%28f%5Fx%2C%20f%5Fy%2C%20c%5Fx%2C%20c%5Fy%2C%20k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%2C%20k%5F3%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%20%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%0A%20s%5F4%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%29) If one of parameters is not estimated, it's deviation is equals to zero.
	/// * stdDeviationsExtrinsics: Output vector of standard deviations estimated for extrinsic
	/// parameters. Order of deviations values: ![inline formula](https://latex.codecogs.com/png.latex?%28R%5F0%2C%20T%5F0%2C%20%5Cdotsc%20%2C%20R%5F%7BM%20%2D%201%7D%2C%20T%5F%7BM%20%2D%201%7D%29) where M is
	/// the number of pattern views. ![inline formula](https://latex.codecogs.com/png.latex?R%5Fi%2C%20T%5Fi) are concatenated 1x3 vectors.
	/// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of the following values:
	/// *   [CALIB_USE_INTRINSIC_GUESS] cameraMatrix contains valid initial values of
	/// fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
	/// center ( imageSize is used), and focal distances are computed in a least-squares fashion.
	/// Note, that if intrinsic parameters are known, there is no need to use this function just to
	/// estimate extrinsic parameters. Use [solvePnP] instead.
	/// *   [CALIB_FIX_PRINCIPAL_POINT] The principal point is not changed during the global
	/// optimization. It stays at the center or at a different location specified when
	///  [CALIB_USE_INTRINSIC_GUESS] is set too.
	/// *   [CALIB_FIX_ASPECT_RATIO] The functions consider only fy as a free parameter. The
	/// ratio fx/fy stays the same as in the input cameraMatrix . When
	///  [CALIB_USE_INTRINSIC_GUESS] is not set, the actual input values of fx and fy are
	/// ignored, only their ratio is computed and used further.
	/// *   [CALIB_ZERO_TANGENT_DIST] Tangential distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%28p%5F1%2C%20p%5F2%29) are set
	/// to zeros and stay zero.
	/// *   [CALIB_FIX_FOCAL_LENGTH] The focal length is not changed during the global optimization if
	///  [CALIB_USE_INTRINSIC_GUESS] is set.
	/// *   [CALIB_FIX_K1],..., [CALIB_FIX_K6] The corresponding radial distortion
	/// coefficient is not changed during the optimization. If [CALIB_USE_INTRINSIC_GUESS] is
	/// set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_RATIONAL_MODEL] Coefficients k4, k5, and k6 are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the rational model and return 8 coefficients or more.
	/// *   [CALIB_THIN_PRISM_MODEL] Coefficients s1, s2, s3 and s4 are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the thin prism model and return 12 coefficients or more.
	/// *   [CALIB_FIX_S1_S2_S3_S4] The thin prism distortion coefficients are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_TILTED_MODEL] Coefficients tauX and tauY are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the tilted sensor model and return 14 coefficients.
	/// *   [CALIB_FIX_TAUX_TAUY] The coefficients of the tilted sensor model are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// ## Returns
	/// the overall RMS re-projection error.
	///
	/// The function estimates the intrinsic camera parameters and extrinsic parameters for each of the
	/// views. The algorithm is based on [Zhang2000](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Zhang2000) and [BouguetMCT](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_BouguetMCT) . The coordinates of 3D object
	/// points and their corresponding 2D projections in each view must be specified. That may be achieved
	/// by using an object with known geometry and easily detectable feature points. Such an object is
	/// called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as
	/// a calibration rig (see [findChessboardCorners]). Currently, initialization of intrinsic
	/// parameters (when [CALIB_USE_INTRINSIC_GUESS] is not set) is only implemented for planar calibration
	/// patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also
	/// be used as long as initial cameraMatrix is provided.
	///
	/// The algorithm performs the following steps:
	///
	/// *   Compute the initial intrinsic parameters (the option only available for planar calibration
	///    patterns) or read them from the input parameters. The distortion coefficients are all set to
	///    zeros initially unless some of CALIB_FIX_K? are specified.
	///
	/// *   Estimate the initial camera pose as if the intrinsic parameters have been already known. This is
	///    done using [solvePnP] .
	///
	/// *   Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error,
	///    that is, the total sum of squared distances between the observed feature points imagePoints and
	///    the projected (using the current estimates for camera parameters and the poses) object points
	///    objectPoints. See [projectPoints] for details.
	///
	///
	/// Note:
	///    If you use a non-square (i.e. non-N-by-N) grid and [findChessboardCorners] for calibration,
	///    and [calibrateCamera] returns bad values (zero distortion coefficients, ![inline formula](https://latex.codecogs.com/png.latex?c%5Fx) and
	///    ![inline formula](https://latex.codecogs.com/png.latex?c%5Fy) very far from the image center, and/or large differences between ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx) and
	///    ![inline formula](https://latex.codecogs.com/png.latex?f%5Fy) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols)
	///    instead of using patternSize=cvSize(cols,rows) in [findChessboardCorners].
	///
	///
	/// Note:
	///    The function may throw exceptions, if unsupported combination of parameters is provided or
	///    the system is underconstrained.
	/// ## See also
	/// calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate,
	///    undistort
	///
	/// ## Note
	/// This alternative version of [calibrate_camera_extended] function uses the following default values for its arguments:
	/// * flags: 0
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,30,DBL_EPSILON)
	#[inline]
	pub fn calibrate_camera_extended_def(object_points: &impl ToInputArray, image_points: &impl ToInputArray, image_size: core::Size, camera_matrix: &mut impl ToInputOutputArray, dist_coeffs: &mut impl ToInputOutputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray, std_deviations_intrinsics: &mut impl ToOutputArray, std_deviations_extrinsics: &mut impl ToOutputArray, per_view_errors: &mut impl ToOutputArray) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_output_array_arg!(camera_matrix);
		input_output_array_arg!(dist_coeffs);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		output_array_arg!(std_deviations_intrinsics);
		output_array_arg!(std_deviations_extrinsics);
		output_array_arg!(per_view_errors);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_calibrateCamera_const__InputArrayR_const__InputArrayR_Size_const__InputOutputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), &image_size, camera_matrix.as_raw__InputOutputArray(), dist_coeffs.as_raw__InputOutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), std_deviations_intrinsics.as_raw__OutputArray(), std_deviations_extrinsics.as_raw__OutputArray(), per_view_errors.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds the camera intrinsic and extrinsic parameters from several views of a calibration
	/// pattern.
	///
	/// ## Parameters
	/// * objectPoints: In the new interface it is a vector of vectors of calibration pattern points in
	/// the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer
	/// vector contains as many elements as the number of pattern views. If the same calibration pattern
	/// is shown in each view and it is fully visible, all the vectors will be the same. Although, it is
	/// possible to use partially occluded patterns or even different patterns in different views. Then,
	/// the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's
	/// XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig.
	/// In the old interface all the vectors of object points from different views are concatenated
	/// together.
	/// * imagePoints: In the new interface it is a vector of vectors of the projections of calibration
	/// pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and
	/// objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal,
	/// respectively. In the old interface all the vectors of object points from different views are
	/// concatenated together.
	/// * imageSize: Size of the image used only to initialize the camera intrinsic matrix.
	/// * cameraMatrix: Input/output 3x3 floating-point camera intrinsic matrix
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) . If [CALIB_USE_INTRINSIC_GUESS]
	/// and/or [CALIB_FIX_ASPECT_RATIO], [CALIB_FIX_PRINCIPAL_POINT] or [CALIB_FIX_FOCAL_LENGTH]
	/// are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
	/// * distCoeffs: Input/output vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs).
	/// * rvecs: Output vector of rotation vectors ([Rodrigues] ) estimated for each pattern view
	/// (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding
	/// i-th translation vector (see the next output parameter description) brings the calibration pattern
	/// from the object coordinate space (in which object points are specified) to the camera coordinate
	/// space. In more technical terms, the tuple of the i-th rotation and translation vector performs
	/// a change of basis from object coordinate space to camera coordinate space. Due to its duality, this
	/// tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate
	/// space.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view, see parameter
	/// describtion above.
	/// * stdDeviationsIntrinsics: Output vector of standard deviations estimated for intrinsic
	/// parameters. Order of deviations values:
	/// ![inline formula](https://latex.codecogs.com/png.latex?%28f%5Fx%2C%20f%5Fy%2C%20c%5Fx%2C%20c%5Fy%2C%20k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%2C%20k%5F3%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%20%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%0A%20s%5F4%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%29) If one of parameters is not estimated, it's deviation is equals to zero.
	/// * stdDeviationsExtrinsics: Output vector of standard deviations estimated for extrinsic
	/// parameters. Order of deviations values: ![inline formula](https://latex.codecogs.com/png.latex?%28R%5F0%2C%20T%5F0%2C%20%5Cdotsc%20%2C%20R%5F%7BM%20%2D%201%7D%2C%20T%5F%7BM%20%2D%201%7D%29) where M is
	/// the number of pattern views. ![inline formula](https://latex.codecogs.com/png.latex?R%5Fi%2C%20T%5Fi) are concatenated 1x3 vectors.
	/// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of the following values:
	/// *   [CALIB_USE_INTRINSIC_GUESS] cameraMatrix contains valid initial values of
	/// fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
	/// center ( imageSize is used), and focal distances are computed in a least-squares fashion.
	/// Note, that if intrinsic parameters are known, there is no need to use this function just to
	/// estimate extrinsic parameters. Use [solvePnP] instead.
	/// *   [CALIB_FIX_PRINCIPAL_POINT] The principal point is not changed during the global
	/// optimization. It stays at the center or at a different location specified when
	///  [CALIB_USE_INTRINSIC_GUESS] is set too.
	/// *   [CALIB_FIX_ASPECT_RATIO] The functions consider only fy as a free parameter. The
	/// ratio fx/fy stays the same as in the input cameraMatrix . When
	///  [CALIB_USE_INTRINSIC_GUESS] is not set, the actual input values of fx and fy are
	/// ignored, only their ratio is computed and used further.
	/// *   [CALIB_ZERO_TANGENT_DIST] Tangential distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%28p%5F1%2C%20p%5F2%29) are set
	/// to zeros and stay zero.
	/// *   [CALIB_FIX_FOCAL_LENGTH] The focal length is not changed during the global optimization if
	///  [CALIB_USE_INTRINSIC_GUESS] is set.
	/// *   [CALIB_FIX_K1],..., [CALIB_FIX_K6] The corresponding radial distortion
	/// coefficient is not changed during the optimization. If [CALIB_USE_INTRINSIC_GUESS] is
	/// set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_RATIONAL_MODEL] Coefficients k4, k5, and k6 are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the rational model and return 8 coefficients or more.
	/// *   [CALIB_THIN_PRISM_MODEL] Coefficients s1, s2, s3 and s4 are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the thin prism model and return 12 coefficients or more.
	/// *   [CALIB_FIX_S1_S2_S3_S4] The thin prism distortion coefficients are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_TILTED_MODEL] Coefficients tauX and tauY are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the tilted sensor model and return 14 coefficients.
	/// *   [CALIB_FIX_TAUX_TAUY] The coefficients of the tilted sensor model are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// ## Returns
	/// the overall RMS re-projection error.
	///
	/// The function estimates the intrinsic camera parameters and extrinsic parameters for each of the
	/// views. The algorithm is based on [Zhang2000](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Zhang2000) and [BouguetMCT](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_BouguetMCT) . The coordinates of 3D object
	/// points and their corresponding 2D projections in each view must be specified. That may be achieved
	/// by using an object with known geometry and easily detectable feature points. Such an object is
	/// called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as
	/// a calibration rig (see [findChessboardCorners]). Currently, initialization of intrinsic
	/// parameters (when [CALIB_USE_INTRINSIC_GUESS] is not set) is only implemented for planar calibration
	/// patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also
	/// be used as long as initial cameraMatrix is provided.
	///
	/// The algorithm performs the following steps:
	///
	/// *   Compute the initial intrinsic parameters (the option only available for planar calibration
	///    patterns) or read them from the input parameters. The distortion coefficients are all set to
	///    zeros initially unless some of CALIB_FIX_K? are specified.
	///
	/// *   Estimate the initial camera pose as if the intrinsic parameters have been already known. This is
	///    done using [solvePnP] .
	///
	/// *   Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error,
	///    that is, the total sum of squared distances between the observed feature points imagePoints and
	///    the projected (using the current estimates for camera parameters and the poses) object points
	///    objectPoints. See [projectPoints] for details.
	///
	///
	/// Note:
	///    If you use a non-square (i.e. non-N-by-N) grid and [findChessboardCorners] for calibration,
	///    and [calibrateCamera] returns bad values (zero distortion coefficients, ![inline formula](https://latex.codecogs.com/png.latex?c%5Fx) and
	///    ![inline formula](https://latex.codecogs.com/png.latex?c%5Fy) very far from the image center, and/or large differences between ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx) and
	///    ![inline formula](https://latex.codecogs.com/png.latex?f%5Fy) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols)
	///    instead of using patternSize=cvSize(cols,rows) in [findChessboardCorners].
	///
	///
	/// Note:
	///    The function may throw exceptions, if unsupported combination of parameters is provided or
	///    the system is underconstrained.
	/// ## See also
	/// calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate,
	///    undistort
	///
	/// ## C++ default parameters
	/// * flags: 0
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,30,DBL_EPSILON)
	#[inline]
	pub fn calibrate_camera_extended(object_points: &impl ToInputArray, image_points: &impl ToInputArray, image_size: core::Size, camera_matrix: &mut impl ToInputOutputArray, dist_coeffs: &mut impl ToInputOutputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray, std_deviations_intrinsics: &mut impl ToOutputArray, std_deviations_extrinsics: &mut impl ToOutputArray, per_view_errors: &mut impl ToOutputArray, flags: i32, criteria: core::TermCriteria) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_output_array_arg!(camera_matrix);
		input_output_array_arg!(dist_coeffs);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		output_array_arg!(std_deviations_intrinsics);
		output_array_arg!(std_deviations_extrinsics);
		output_array_arg!(per_view_errors);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_calibrateCamera_const__InputArrayR_const__InputArrayR_Size_const__InputOutputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_TermCriteria(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), &image_size, camera_matrix.as_raw__InputOutputArray(), dist_coeffs.as_raw__InputOutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), std_deviations_intrinsics.as_raw__OutputArray(), std_deviations_extrinsics.as_raw__OutputArray(), per_view_errors.as_raw__OutputArray(), flags, &criteria, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds the camera intrinsic and extrinsic parameters from several views of a calibration
	/// pattern.
	///
	/// ## Parameters
	/// * objectPoints: In the new interface it is a vector of vectors of calibration pattern points in
	/// the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer
	/// vector contains as many elements as the number of pattern views. If the same calibration pattern
	/// is shown in each view and it is fully visible, all the vectors will be the same. Although, it is
	/// possible to use partially occluded patterns or even different patterns in different views. Then,
	/// the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's
	/// XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig.
	/// In the old interface all the vectors of object points from different views are concatenated
	/// together.
	/// * imagePoints: In the new interface it is a vector of vectors of the projections of calibration
	/// pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and
	/// objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal,
	/// respectively. In the old interface all the vectors of object points from different views are
	/// concatenated together.
	/// * imageSize: Size of the image used only to initialize the camera intrinsic matrix.
	/// * cameraMatrix: Input/output 3x3 floating-point camera intrinsic matrix
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) . If [CALIB_USE_INTRINSIC_GUESS]
	/// and/or [CALIB_FIX_ASPECT_RATIO], [CALIB_FIX_PRINCIPAL_POINT] or [CALIB_FIX_FOCAL_LENGTH]
	/// are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
	/// * distCoeffs: Input/output vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs).
	/// * rvecs: Output vector of rotation vectors ([Rodrigues] ) estimated for each pattern view
	/// (e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding
	/// i-th translation vector (see the next output parameter description) brings the calibration pattern
	/// from the object coordinate space (in which object points are specified) to the camera coordinate
	/// space. In more technical terms, the tuple of the i-th rotation and translation vector performs
	/// a change of basis from object coordinate space to camera coordinate space. Due to its duality, this
	/// tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate
	/// space.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view, see parameter
	/// describtion above.
	/// * stdDeviationsIntrinsics: Output vector of standard deviations estimated for intrinsic
	/// parameters. Order of deviations values:
	/// ![inline formula](https://latex.codecogs.com/png.latex?%28f%5Fx%2C%20f%5Fy%2C%20c%5Fx%2C%20c%5Fy%2C%20k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%2C%20k%5F3%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%20%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%0A%20s%5F4%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%29) If one of parameters is not estimated, it's deviation is equals to zero.
	/// * stdDeviationsExtrinsics: Output vector of standard deviations estimated for extrinsic
	/// parameters. Order of deviations values: ![inline formula](https://latex.codecogs.com/png.latex?%28R%5F0%2C%20T%5F0%2C%20%5Cdotsc%20%2C%20R%5F%7BM%20%2D%201%7D%2C%20T%5F%7BM%20%2D%201%7D%29) where M is
	/// the number of pattern views. ![inline formula](https://latex.codecogs.com/png.latex?R%5Fi%2C%20T%5Fi) are concatenated 1x3 vectors.
	/// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of the following values:
	/// *   [CALIB_USE_INTRINSIC_GUESS] cameraMatrix contains valid initial values of
	/// fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
	/// center ( imageSize is used), and focal distances are computed in a least-squares fashion.
	/// Note, that if intrinsic parameters are known, there is no need to use this function just to
	/// estimate extrinsic parameters. Use [solvePnP] instead.
	/// *   [CALIB_FIX_PRINCIPAL_POINT] The principal point is not changed during the global
	/// optimization. It stays at the center or at a different location specified when
	///  [CALIB_USE_INTRINSIC_GUESS] is set too.
	/// *   [CALIB_FIX_ASPECT_RATIO] The functions consider only fy as a free parameter. The
	/// ratio fx/fy stays the same as in the input cameraMatrix . When
	///  [CALIB_USE_INTRINSIC_GUESS] is not set, the actual input values of fx and fy are
	/// ignored, only their ratio is computed and used further.
	/// *   [CALIB_ZERO_TANGENT_DIST] Tangential distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%28p%5F1%2C%20p%5F2%29) are set
	/// to zeros and stay zero.
	/// *   [CALIB_FIX_FOCAL_LENGTH] The focal length is not changed during the global optimization if
	///  [CALIB_USE_INTRINSIC_GUESS] is set.
	/// *   [CALIB_FIX_K1],..., [CALIB_FIX_K6] The corresponding radial distortion
	/// coefficient is not changed during the optimization. If [CALIB_USE_INTRINSIC_GUESS] is
	/// set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_RATIONAL_MODEL] Coefficients k4, k5, and k6 are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the rational model and return 8 coefficients or more.
	/// *   [CALIB_THIN_PRISM_MODEL] Coefficients s1, s2, s3 and s4 are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the thin prism model and return 12 coefficients or more.
	/// *   [CALIB_FIX_S1_S2_S3_S4] The thin prism distortion coefficients are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_TILTED_MODEL] Coefficients tauX and tauY are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the tilted sensor model and return 14 coefficients.
	/// *   [CALIB_FIX_TAUX_TAUY] The coefficients of the tilted sensor model are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// ## Returns
	/// the overall RMS re-projection error.
	///
	/// The function estimates the intrinsic camera parameters and extrinsic parameters for each of the
	/// views. The algorithm is based on [Zhang2000](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Zhang2000) and [BouguetMCT](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_BouguetMCT) . The coordinates of 3D object
	/// points and their corresponding 2D projections in each view must be specified. That may be achieved
	/// by using an object with known geometry and easily detectable feature points. Such an object is
	/// called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as
	/// a calibration rig (see [findChessboardCorners]). Currently, initialization of intrinsic
	/// parameters (when [CALIB_USE_INTRINSIC_GUESS] is not set) is only implemented for planar calibration
	/// patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also
	/// be used as long as initial cameraMatrix is provided.
	///
	/// The algorithm performs the following steps:
	///
	/// *   Compute the initial intrinsic parameters (the option only available for planar calibration
	///    patterns) or read them from the input parameters. The distortion coefficients are all set to
	///    zeros initially unless some of CALIB_FIX_K? are specified.
	///
	/// *   Estimate the initial camera pose as if the intrinsic parameters have been already known. This is
	///    done using [solvePnP] .
	///
	/// *   Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error,
	///    that is, the total sum of squared distances between the observed feature points imagePoints and
	///    the projected (using the current estimates for camera parameters and the poses) object points
	///    objectPoints. See [projectPoints] for details.
	///
	///
	/// Note:
	///    If you use a non-square (i.e. non-N-by-N) grid and [findChessboardCorners] for calibration,
	///    and [calibrateCamera] returns bad values (zero distortion coefficients, ![inline formula](https://latex.codecogs.com/png.latex?c%5Fx) and
	///    ![inline formula](https://latex.codecogs.com/png.latex?c%5Fy) very far from the image center, and/or large differences between ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx) and
	///    ![inline formula](https://latex.codecogs.com/png.latex?f%5Fy) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols)
	///    instead of using patternSize=cvSize(cols,rows) in [findChessboardCorners].
	///
	///
	/// Note:
	///    The function may throw exceptions, if unsupported combination of parameters is provided or
	///    the system is underconstrained.
	/// ## See also
	/// calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate,
	///    undistort
	///
	/// ## Overloaded parameters
	///
	/// ## C++ default parameters
	/// * flags: 0
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,30,DBL_EPSILON)
	#[inline]
	pub fn calibrate_camera(object_points: &impl ToInputArray, image_points: &impl ToInputArray, image_size: core::Size, camera_matrix: &mut impl ToInputOutputArray, dist_coeffs: &mut impl ToInputOutputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray, flags: i32, criteria: core::TermCriteria) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_output_array_arg!(camera_matrix);
		input_output_array_arg!(dist_coeffs);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_calibrateCamera_const__InputArrayR_const__InputArrayR_Size_const__InputOutputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_int_TermCriteria(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), &image_size, camera_matrix.as_raw__InputOutputArray(), dist_coeffs.as_raw__InputOutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), flags, &criteria, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes Hand-Eye calibration: ![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fc)
	///
	/// ## Parameters
	/// * R_gripper2base: Rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the gripper frame to the robot base frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bb%7D%5Ctextrm%7BT%7D%5Fg)).
	/// This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
	/// for all the transformations from gripper frame to robot base frame.
	/// * t_gripper2base: Translation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the gripper frame to the robot base frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bb%7D%5Ctextrm%7BT%7D%5Fg)).
	/// This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
	/// from gripper frame to robot base frame.
	/// * R_target2cam: Rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the target frame to the camera frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Ft)).
	/// This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
	/// for all the transformations from calibration target frame to camera frame.
	/// * t_target2cam: Rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the target frame to the camera frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Ft)).
	/// This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
	/// from calibration target frame to camera frame.
	/// * R_cam2gripper:[out] Estimated `(3x3)` rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the camera frame to the gripper frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fc)).
	/// * t_cam2gripper:[out] Estimated `(3x1)` translation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the camera frame to the gripper frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fc)).
	/// * method: One of the implemented Hand-Eye calibration method, see cv::HandEyeCalibrationMethod
	///
	/// The function performs the Hand-Eye calibration using various methods. One approach consists in estimating the
	/// rotation then the translation (separable solutions) and the following methods are implemented:
	///   - R. Tsai, R. Lenz A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/EyeCalibration \cite Tsai89
	///   - F. Park, B. Martin Robot Sensor Calibration: Solving AX = XB on the Euclidean Group \cite Park94
	///   - R. Horaud, F. Dornaika Hand-Eye Calibration \cite Horaud95
	///
	/// Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions),
	/// with the following implemented methods:
	///   - N. Andreff, R. Horaud, B. Espiau On-line Hand-Eye Calibration \cite Andreff99
	///   - K. Daniilidis Hand-Eye Calibration Using Dual Quaternions \cite Daniilidis98
	///
	/// The following picture describes the Hand-Eye calibration problem where the transformation between a camera ("eye")
	/// mounted on a robot gripper ("hand") has to be estimated. This configuration is called eye-in-hand.
	///
	/// The eye-to-hand configuration consists in a static camera observing a calibration pattern mounted on the robot
	/// end-effector. The transformation from the camera to the robot base frame can then be estimated by inputting
	/// the suitable transformations to the function, see below.
	///
	/// ![](https://docs.opencv.org/4.12.0/hand-eye_figure.png)
	///
	/// The calibration procedure is the following:
	///   - a static calibration pattern is used to estimate the transformation between the target frame
	///   and the camera frame
	///   - the robot gripper is moved in order to acquire several poses
	///   - for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for
	///   instance the robot kinematics
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fb%5C%5C%0A%20%20%20%20Y%5Fb%5C%5C%0A%20%20%20%20Z%5Fb%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%3D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%5F%7B%7D%5E%7Bb%7D%5Ctextrm%7BR%7D%5Fg%20%26%20%5F%7B%7D%5E%7Bb%7D%5Ctextrm%7Bt%7D%5Fg%20%5C%5C%0A%20%20%20%200%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fg%5C%5C%0A%20%20%20%20Y%5Fg%5C%5C%0A%20%20%20%20Z%5Fg%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A)
	///   - for each pose, the homogeneous transformation between the calibration target frame and the camera frame is recorded using
	///   for instance a pose estimation method (PnP) from 2D-3D point correspondences
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fc%5C%5C%0A%20%20%20%20Y%5Fc%5C%5C%0A%20%20%20%20Z%5Fc%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%3D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BR%7D%5Ft%20%26%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7Bt%7D%5Ft%20%5C%5C%0A%20%20%20%200%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Ft%5C%5C%0A%20%20%20%20Y%5Ft%5C%5C%0A%20%20%20%20Z%5Ft%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A)
	///
	/// The Hand-Eye calibration procedure returns the following homogeneous transformation
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fg%5C%5C%0A%20%20%20%20Y%5Fg%5C%5C%0A%20%20%20%20Z%5Fg%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%3D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BR%7D%5Fc%20%26%20%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7Bt%7D%5Fc%20%5C%5C%0A%20%20%20%200%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fc%5C%5C%0A%20%20%20%20Y%5Fc%5C%5C%0A%20%20%20%20Z%5Fc%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A)
	///
	/// This problem is also known as solving the ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathbf%7BA%7D%5Cmathbf%7BX%7D%3D%5Cmathbf%7BX%7D%5Cmathbf%7BB%7D) equation:
	///   - for an eye-in-hand configuration
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Balign%2A%7D%0A%20%20%20%20%5E%7Bb%7D%7B%5Ctextrm%7BT%7D%5Fg%7D%5E%7B%281%29%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fc%20%5Chspace%7B0%2E2em%7D%20%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%281%29%7D%20%26%3D%0A%20%20%20%20%5Chspace%7B0%2E1em%7D%20%5E%7Bb%7D%7B%5Ctextrm%7BT%7D%5Fg%7D%5E%7B%282%29%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fc%20%5Chspace%7B0%2E2em%7D%20%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%282%29%7D%20%5C%5C%0A%0A%20%20%20%20%28%5E%7Bb%7D%7B%5Ctextrm%7BT%7D%5Fg%7D%5E%7B%282%29%7D%29%5E%7B%2D1%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bb%7D%7B%5Ctextrm%7BT%7D%5Fg%7D%5E%7B%281%29%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fc%20%26%3D%0A%20%20%20%20%5Chspace%7B0%2E1em%7D%20%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fc%20%5Chspace%7B0%2E2em%7D%20%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%282%29%7D%20%28%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%281%29%7D%29%5E%7B%2D1%7D%20%5C%5C%0A%0A%20%20%20%20%5Ctextrm%7BA%7D%5Fi%20%5Ctextrm%7BX%7D%20%26%3D%20%5Ctextrm%7BX%7D%20%5Ctextrm%7BB%7D%5Fi%20%5C%5C%0A%20%20%20%20%5Cend%7Balign%2A%7D%0A)
	///
	///   - for an eye-to-hand configuration
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Balign%2A%7D%0A%20%20%20%20%5E%7Bg%7D%7B%5Ctextrm%7BT%7D%5Fb%7D%5E%7B%281%29%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bb%7D%5Ctextrm%7BT%7D%5Fc%20%5Chspace%7B0%2E2em%7D%20%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%281%29%7D%20%26%3D%0A%20%20%20%20%5Chspace%7B0%2E1em%7D%20%5E%7Bg%7D%7B%5Ctextrm%7BT%7D%5Fb%7D%5E%7B%282%29%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bb%7D%5Ctextrm%7BT%7D%5Fc%20%5Chspace%7B0%2E2em%7D%20%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%282%29%7D%20%5C%5C%0A%0A%20%20%20%20%28%5E%7Bg%7D%7B%5Ctextrm%7BT%7D%5Fb%7D%5E%7B%282%29%7D%29%5E%7B%2D1%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bg%7D%7B%5Ctextrm%7BT%7D%5Fb%7D%5E%7B%281%29%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bb%7D%5Ctextrm%7BT%7D%5Fc%20%26%3D%0A%20%20%20%20%5Chspace%7B0%2E1em%7D%20%5E%7Bb%7D%5Ctextrm%7BT%7D%5Fc%20%5Chspace%7B0%2E2em%7D%20%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%282%29%7D%20%28%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%281%29%7D%29%5E%7B%2D1%7D%20%5C%5C%0A%0A%20%20%20%20%5Ctextrm%7BA%7D%5Fi%20%5Ctextrm%7BX%7D%20%26%3D%20%5Ctextrm%7BX%7D%20%5Ctextrm%7BB%7D%5Fi%20%5C%5C%0A%20%20%20%20%5Cend%7Balign%2A%7D%0A)
	///
	/// \note
	/// Additional information can be found on this [website](http://campar.in.tum.de/Chair/HandEyeCalibration).
	/// \note
	/// A minimum of 2 motions with non parallel rotation axes are necessary to determine the hand-eye transformation.
	/// So at least 3 different poses are required, but it is strongly recommended to use many more poses.
	///
	/// ## Note
	/// This alternative version of [calibrate_hand_eye] function uses the following default values for its arguments:
	/// * method: CALIB_HAND_EYE_TSAI
	#[inline]
	pub fn calibrate_hand_eye_def(r_gripper2base: &impl ToInputArray, t_gripper2base: &impl ToInputArray, r_target2cam: &impl ToInputArray, t_target2cam: &impl ToInputArray, r_cam2gripper: &mut impl ToOutputArray, t_cam2gripper: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(r_gripper2base);
		input_array_arg!(t_gripper2base);
		input_array_arg!(r_target2cam);
		input_array_arg!(t_target2cam);
		output_array_arg!(r_cam2gripper);
		output_array_arg!(t_cam2gripper);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_calibrateHandEye_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(r_gripper2base.as_raw__InputArray(), t_gripper2base.as_raw__InputArray(), r_target2cam.as_raw__InputArray(), t_target2cam.as_raw__InputArray(), r_cam2gripper.as_raw__OutputArray(), t_cam2gripper.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes Hand-Eye calibration: ![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fc)
	///
	/// ## Parameters
	/// * R_gripper2base: Rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the gripper frame to the robot base frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bb%7D%5Ctextrm%7BT%7D%5Fg)).
	/// This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
	/// for all the transformations from gripper frame to robot base frame.
	/// * t_gripper2base: Translation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the gripper frame to the robot base frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bb%7D%5Ctextrm%7BT%7D%5Fg)).
	/// This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
	/// from gripper frame to robot base frame.
	/// * R_target2cam: Rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the target frame to the camera frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Ft)).
	/// This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
	/// for all the transformations from calibration target frame to camera frame.
	/// * t_target2cam: Rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the target frame to the camera frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Ft)).
	/// This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
	/// from calibration target frame to camera frame.
	/// * R_cam2gripper:[out] Estimated `(3x3)` rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the camera frame to the gripper frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fc)).
	/// * t_cam2gripper:[out] Estimated `(3x1)` translation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the camera frame to the gripper frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fc)).
	/// * method: One of the implemented Hand-Eye calibration method, see cv::HandEyeCalibrationMethod
	///
	/// The function performs the Hand-Eye calibration using various methods. One approach consists in estimating the
	/// rotation then the translation (separable solutions) and the following methods are implemented:
	///   - R. Tsai, R. Lenz A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/EyeCalibration \cite Tsai89
	///   - F. Park, B. Martin Robot Sensor Calibration: Solving AX = XB on the Euclidean Group \cite Park94
	///   - R. Horaud, F. Dornaika Hand-Eye Calibration \cite Horaud95
	///
	/// Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions),
	/// with the following implemented methods:
	///   - N. Andreff, R. Horaud, B. Espiau On-line Hand-Eye Calibration \cite Andreff99
	///   - K. Daniilidis Hand-Eye Calibration Using Dual Quaternions \cite Daniilidis98
	///
	/// The following picture describes the Hand-Eye calibration problem where the transformation between a camera ("eye")
	/// mounted on a robot gripper ("hand") has to be estimated. This configuration is called eye-in-hand.
	///
	/// The eye-to-hand configuration consists in a static camera observing a calibration pattern mounted on the robot
	/// end-effector. The transformation from the camera to the robot base frame can then be estimated by inputting
	/// the suitable transformations to the function, see below.
	///
	/// ![](https://docs.opencv.org/4.12.0/hand-eye_figure.png)
	///
	/// The calibration procedure is the following:
	///   - a static calibration pattern is used to estimate the transformation between the target frame
	///   and the camera frame
	///   - the robot gripper is moved in order to acquire several poses
	///   - for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for
	///   instance the robot kinematics
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fb%5C%5C%0A%20%20%20%20Y%5Fb%5C%5C%0A%20%20%20%20Z%5Fb%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%3D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%5F%7B%7D%5E%7Bb%7D%5Ctextrm%7BR%7D%5Fg%20%26%20%5F%7B%7D%5E%7Bb%7D%5Ctextrm%7Bt%7D%5Fg%20%5C%5C%0A%20%20%20%200%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fg%5C%5C%0A%20%20%20%20Y%5Fg%5C%5C%0A%20%20%20%20Z%5Fg%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A)
	///   - for each pose, the homogeneous transformation between the calibration target frame and the camera frame is recorded using
	///   for instance a pose estimation method (PnP) from 2D-3D point correspondences
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fc%5C%5C%0A%20%20%20%20Y%5Fc%5C%5C%0A%20%20%20%20Z%5Fc%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%3D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BR%7D%5Ft%20%26%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7Bt%7D%5Ft%20%5C%5C%0A%20%20%20%200%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Ft%5C%5C%0A%20%20%20%20Y%5Ft%5C%5C%0A%20%20%20%20Z%5Ft%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A)
	///
	/// The Hand-Eye calibration procedure returns the following homogeneous transformation
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fg%5C%5C%0A%20%20%20%20Y%5Fg%5C%5C%0A%20%20%20%20Z%5Fg%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%3D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BR%7D%5Fc%20%26%20%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7Bt%7D%5Fc%20%5C%5C%0A%20%20%20%200%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fc%5C%5C%0A%20%20%20%20Y%5Fc%5C%5C%0A%20%20%20%20Z%5Fc%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A)
	///
	/// This problem is also known as solving the ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathbf%7BA%7D%5Cmathbf%7BX%7D%3D%5Cmathbf%7BX%7D%5Cmathbf%7BB%7D) equation:
	///   - for an eye-in-hand configuration
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Balign%2A%7D%0A%20%20%20%20%5E%7Bb%7D%7B%5Ctextrm%7BT%7D%5Fg%7D%5E%7B%281%29%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fc%20%5Chspace%7B0%2E2em%7D%20%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%281%29%7D%20%26%3D%0A%20%20%20%20%5Chspace%7B0%2E1em%7D%20%5E%7Bb%7D%7B%5Ctextrm%7BT%7D%5Fg%7D%5E%7B%282%29%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fc%20%5Chspace%7B0%2E2em%7D%20%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%282%29%7D%20%5C%5C%0A%0A%20%20%20%20%28%5E%7Bb%7D%7B%5Ctextrm%7BT%7D%5Fg%7D%5E%7B%282%29%7D%29%5E%7B%2D1%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bb%7D%7B%5Ctextrm%7BT%7D%5Fg%7D%5E%7B%281%29%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fc%20%26%3D%0A%20%20%20%20%5Chspace%7B0%2E1em%7D%20%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fc%20%5Chspace%7B0%2E2em%7D%20%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%282%29%7D%20%28%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%281%29%7D%29%5E%7B%2D1%7D%20%5C%5C%0A%0A%20%20%20%20%5Ctextrm%7BA%7D%5Fi%20%5Ctextrm%7BX%7D%20%26%3D%20%5Ctextrm%7BX%7D%20%5Ctextrm%7BB%7D%5Fi%20%5C%5C%0A%20%20%20%20%5Cend%7Balign%2A%7D%0A)
	///
	///   - for an eye-to-hand configuration
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Balign%2A%7D%0A%20%20%20%20%5E%7Bg%7D%7B%5Ctextrm%7BT%7D%5Fb%7D%5E%7B%281%29%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bb%7D%5Ctextrm%7BT%7D%5Fc%20%5Chspace%7B0%2E2em%7D%20%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%281%29%7D%20%26%3D%0A%20%20%20%20%5Chspace%7B0%2E1em%7D%20%5E%7Bg%7D%7B%5Ctextrm%7BT%7D%5Fb%7D%5E%7B%282%29%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bb%7D%5Ctextrm%7BT%7D%5Fc%20%5Chspace%7B0%2E2em%7D%20%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%282%29%7D%20%5C%5C%0A%0A%20%20%20%20%28%5E%7Bg%7D%7B%5Ctextrm%7BT%7D%5Fb%7D%5E%7B%282%29%7D%29%5E%7B%2D1%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bg%7D%7B%5Ctextrm%7BT%7D%5Fb%7D%5E%7B%281%29%7D%20%5Chspace%7B0%2E2em%7D%20%5E%7Bb%7D%5Ctextrm%7BT%7D%5Fc%20%26%3D%0A%20%20%20%20%5Chspace%7B0%2E1em%7D%20%5E%7Bb%7D%5Ctextrm%7BT%7D%5Fc%20%5Chspace%7B0%2E2em%7D%20%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%282%29%7D%20%28%5E%7Bc%7D%7B%5Ctextrm%7BT%7D%5Ft%7D%5E%7B%281%29%7D%29%5E%7B%2D1%7D%20%5C%5C%0A%0A%20%20%20%20%5Ctextrm%7BA%7D%5Fi%20%5Ctextrm%7BX%7D%20%26%3D%20%5Ctextrm%7BX%7D%20%5Ctextrm%7BB%7D%5Fi%20%5C%5C%0A%20%20%20%20%5Cend%7Balign%2A%7D%0A)
	///
	/// \note
	/// Additional information can be found on this [website](http://campar.in.tum.de/Chair/HandEyeCalibration).
	/// \note
	/// A minimum of 2 motions with non parallel rotation axes are necessary to determine the hand-eye transformation.
	/// So at least 3 different poses are required, but it is strongly recommended to use many more poses.
	///
	/// ## C++ default parameters
	/// * method: CALIB_HAND_EYE_TSAI
	#[inline]
	pub fn calibrate_hand_eye(r_gripper2base: &impl ToInputArray, t_gripper2base: &impl ToInputArray, r_target2cam: &impl ToInputArray, t_target2cam: &impl ToInputArray, r_cam2gripper: &mut impl ToOutputArray, t_cam2gripper: &mut impl ToOutputArray, method: crate::calib3d::HandEyeCalibrationMethod) -> Result<()> {
		input_array_arg!(r_gripper2base);
		input_array_arg!(t_gripper2base);
		input_array_arg!(r_target2cam);
		input_array_arg!(t_target2cam);
		output_array_arg!(r_cam2gripper);
		output_array_arg!(t_cam2gripper);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_calibrateHandEye_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_HandEyeCalibrationMethod(r_gripper2base.as_raw__InputArray(), t_gripper2base.as_raw__InputArray(), r_target2cam.as_raw__InputArray(), t_target2cam.as_raw__InputArray(), r_cam2gripper.as_raw__OutputArray(), t_cam2gripper.as_raw__OutputArray(), method, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes Robot-World/Hand-Eye calibration: ![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bw%7D%5Ctextrm%7BT%7D%5Fb) and ![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Fg)
	///
	/// ## Parameters
	/// * R_world2cam: Rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the world frame to the camera frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Fw)).
	/// This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
	/// for all the transformations from world frame to the camera frame.
	/// * t_world2cam: Translation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the world frame to the camera frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Fw)).
	/// This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
	/// from world frame to the camera frame.
	/// * R_base2gripper: Rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the robot base frame to the gripper frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fb)).
	/// This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
	/// for all the transformations from robot base frame to the gripper frame.
	/// * t_base2gripper: Rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the robot base frame to the gripper frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fb)).
	/// This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
	/// from robot base frame to the gripper frame.
	/// * R_base2world:[out] Estimated `(3x3)` rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the robot base frame to the world frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bw%7D%5Ctextrm%7BT%7D%5Fb)).
	/// * t_base2world:[out] Estimated `(3x1)` translation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the robot base frame to the world frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bw%7D%5Ctextrm%7BT%7D%5Fb)).
	/// * R_gripper2cam:[out] Estimated `(3x3)` rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the gripper frame to the camera frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Fg)).
	/// * t_gripper2cam:[out] Estimated `(3x1)` translation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the gripper frame to the camera frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Fg)).
	/// * method: One of the implemented Robot-World/Hand-Eye calibration method, see cv::RobotWorldHandEyeCalibrationMethod
	///
	/// The function performs the Robot-World/Hand-Eye calibration using various methods. One approach consists in estimating the
	/// rotation then the translation (separable solutions):
	///   - M. Shah, Solving the robot-world/hand-eye calibration problem using the kronecker product \cite Shah2013SolvingTR
	///
	/// Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions),
	/// with the following implemented method:
	///   - A. Li, L. Wang, and D. Wu, Simultaneous robot-world and hand-eye calibration using dual-quaternions and kronecker product \cite Li2010SimultaneousRA
	///
	/// The following picture describes the Robot-World/Hand-Eye calibration problem where the transformations between a robot and a world frame
	/// and between a robot gripper ("hand") and a camera ("eye") mounted at the robot end-effector have to be estimated.
	///
	/// ![](https://docs.opencv.org/4.12.0/robot-world_hand-eye_figure.png)
	///
	/// The calibration procedure is the following:
	///   - a static calibration pattern is used to estimate the transformation between the target frame
	///   and the camera frame
	///   - the robot gripper is moved in order to acquire several poses
	///   - for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for
	///   instance the robot kinematics
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fg%5C%5C%0A%20%20%20%20Y%5Fg%5C%5C%0A%20%20%20%20Z%5Fg%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%3D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BR%7D%5Fb%20%26%20%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7Bt%7D%5Fb%20%5C%5C%0A%20%20%20%200%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fb%5C%5C%0A%20%20%20%20Y%5Fb%5C%5C%0A%20%20%20%20Z%5Fb%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A)
	///   - for each pose, the homogeneous transformation between the calibration target frame (the world frame) and the camera frame is recorded using
	///   for instance a pose estimation method (PnP) from 2D-3D point correspondences
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fc%5C%5C%0A%20%20%20%20Y%5Fc%5C%5C%0A%20%20%20%20Z%5Fc%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%3D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BR%7D%5Fw%20%26%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7Bt%7D%5Fw%20%5C%5C%0A%20%20%20%200%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fw%5C%5C%0A%20%20%20%20Y%5Fw%5C%5C%0A%20%20%20%20Z%5Fw%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A)
	///
	/// The Robot-World/Hand-Eye calibration procedure returns the following homogeneous transformations
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fw%5C%5C%0A%20%20%20%20Y%5Fw%5C%5C%0A%20%20%20%20Z%5Fw%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%3D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%5F%7B%7D%5E%7Bw%7D%5Ctextrm%7BR%7D%5Fb%20%26%20%5F%7B%7D%5E%7Bw%7D%5Ctextrm%7Bt%7D%5Fb%20%5C%5C%0A%20%20%20%200%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fb%5C%5C%0A%20%20%20%20Y%5Fb%5C%5C%0A%20%20%20%20Z%5Fb%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A)
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fc%5C%5C%0A%20%20%20%20Y%5Fc%5C%5C%0A%20%20%20%20Z%5Fc%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%3D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BR%7D%5Fg%20%26%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7Bt%7D%5Fg%20%5C%5C%0A%20%20%20%200%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fg%5C%5C%0A%20%20%20%20Y%5Fg%5C%5C%0A%20%20%20%20Z%5Fg%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A)
	///
	/// This problem is also known as solving the ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathbf%7BA%7D%5Cmathbf%7BX%7D%3D%5Cmathbf%7BZ%7D%5Cmathbf%7BB%7D) equation, with:
	///   - ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathbf%7BA%7D%20%5CLeftrightarrow%20%5Chspace%7B0%2E1em%7D%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Fw)
	///   - ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathbf%7BX%7D%20%5CLeftrightarrow%20%5Chspace%7B0%2E1em%7D%20%5F%7B%7D%5E%7Bw%7D%5Ctextrm%7BT%7D%5Fb)
	///   - ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathbf%7BZ%7D%20%5CLeftrightarrow%20%5Chspace%7B0%2E1em%7D%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Fg)
	///   - ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathbf%7BB%7D%20%5CLeftrightarrow%20%5Chspace%7B0%2E1em%7D%20%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fb)
	///
	/// \note
	/// At least 3 measurements are required (input vectors size must be greater or equal to 3).
	///
	/// ## Note
	/// This alternative version of [calibrate_robot_world_hand_eye] function uses the following default values for its arguments:
	/// * method: CALIB_ROBOT_WORLD_HAND_EYE_SHAH
	#[inline]
	pub fn calibrate_robot_world_hand_eye_def(r_world2cam: &impl ToInputArray, t_world2cam: &impl ToInputArray, r_base2gripper: &impl ToInputArray, t_base2gripper: &impl ToInputArray, r_base2world: &mut impl ToOutputArray, t_base2world: &mut impl ToOutputArray, r_gripper2cam: &mut impl ToOutputArray, t_gripper2cam: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(r_world2cam);
		input_array_arg!(t_world2cam);
		input_array_arg!(r_base2gripper);
		input_array_arg!(t_base2gripper);
		output_array_arg!(r_base2world);
		output_array_arg!(t_base2world);
		output_array_arg!(r_gripper2cam);
		output_array_arg!(t_gripper2cam);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_calibrateRobotWorldHandEye_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(r_world2cam.as_raw__InputArray(), t_world2cam.as_raw__InputArray(), r_base2gripper.as_raw__InputArray(), t_base2gripper.as_raw__InputArray(), r_base2world.as_raw__OutputArray(), t_base2world.as_raw__OutputArray(), r_gripper2cam.as_raw__OutputArray(), t_gripper2cam.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes Robot-World/Hand-Eye calibration: ![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bw%7D%5Ctextrm%7BT%7D%5Fb) and ![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Fg)
	///
	/// ## Parameters
	/// * R_world2cam: Rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the world frame to the camera frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Fw)).
	/// This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
	/// for all the transformations from world frame to the camera frame.
	/// * t_world2cam: Translation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the world frame to the camera frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Fw)).
	/// This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
	/// from world frame to the camera frame.
	/// * R_base2gripper: Rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the robot base frame to the gripper frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fb)).
	/// This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
	/// for all the transformations from robot base frame to the gripper frame.
	/// * t_base2gripper: Rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the robot base frame to the gripper frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fb)).
	/// This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
	/// from robot base frame to the gripper frame.
	/// * R_base2world:[out] Estimated `(3x3)` rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the robot base frame to the world frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bw%7D%5Ctextrm%7BT%7D%5Fb)).
	/// * t_base2world:[out] Estimated `(3x1)` translation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the robot base frame to the world frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bw%7D%5Ctextrm%7BT%7D%5Fb)).
	/// * R_gripper2cam:[out] Estimated `(3x3)` rotation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the gripper frame to the camera frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Fg)).
	/// * t_gripper2cam:[out] Estimated `(3x1)` translation part extracted from the homogeneous matrix that transforms a point
	/// expressed in the gripper frame to the camera frame (![inline formula](https://latex.codecogs.com/png.latex?%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Fg)).
	/// * method: One of the implemented Robot-World/Hand-Eye calibration method, see cv::RobotWorldHandEyeCalibrationMethod
	///
	/// The function performs the Robot-World/Hand-Eye calibration using various methods. One approach consists in estimating the
	/// rotation then the translation (separable solutions):
	///   - M. Shah, Solving the robot-world/hand-eye calibration problem using the kronecker product \cite Shah2013SolvingTR
	///
	/// Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions),
	/// with the following implemented method:
	///   - A. Li, L. Wang, and D. Wu, Simultaneous robot-world and hand-eye calibration using dual-quaternions and kronecker product \cite Li2010SimultaneousRA
	///
	/// The following picture describes the Robot-World/Hand-Eye calibration problem where the transformations between a robot and a world frame
	/// and between a robot gripper ("hand") and a camera ("eye") mounted at the robot end-effector have to be estimated.
	///
	/// ![](https://docs.opencv.org/4.12.0/robot-world_hand-eye_figure.png)
	///
	/// The calibration procedure is the following:
	///   - a static calibration pattern is used to estimate the transformation between the target frame
	///   and the camera frame
	///   - the robot gripper is moved in order to acquire several poses
	///   - for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for
	///   instance the robot kinematics
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fg%5C%5C%0A%20%20%20%20Y%5Fg%5C%5C%0A%20%20%20%20Z%5Fg%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%3D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BR%7D%5Fb%20%26%20%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7Bt%7D%5Fb%20%5C%5C%0A%20%20%20%200%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fb%5C%5C%0A%20%20%20%20Y%5Fb%5C%5C%0A%20%20%20%20Z%5Fb%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A)
	///   - for each pose, the homogeneous transformation between the calibration target frame (the world frame) and the camera frame is recorded using
	///   for instance a pose estimation method (PnP) from 2D-3D point correspondences
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fc%5C%5C%0A%20%20%20%20Y%5Fc%5C%5C%0A%20%20%20%20Z%5Fc%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%3D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BR%7D%5Fw%20%26%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7Bt%7D%5Fw%20%5C%5C%0A%20%20%20%200%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fw%5C%5C%0A%20%20%20%20Y%5Fw%5C%5C%0A%20%20%20%20Z%5Fw%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A)
	///
	/// The Robot-World/Hand-Eye calibration procedure returns the following homogeneous transformations
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fw%5C%5C%0A%20%20%20%20Y%5Fw%5C%5C%0A%20%20%20%20Z%5Fw%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%3D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%5F%7B%7D%5E%7Bw%7D%5Ctextrm%7BR%7D%5Fb%20%26%20%5F%7B%7D%5E%7Bw%7D%5Ctextrm%7Bt%7D%5Fb%20%5C%5C%0A%20%20%20%200%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fb%5C%5C%0A%20%20%20%20Y%5Fb%5C%5C%0A%20%20%20%20Z%5Fb%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A)
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fc%5C%5C%0A%20%20%20%20Y%5Fc%5C%5C%0A%20%20%20%20Z%5Fc%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%3D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BR%7D%5Fg%20%26%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7Bt%7D%5Fg%20%5C%5C%0A%20%20%20%200%5F%7B1%20%5Ctimes%203%7D%20%26%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A%20%20%20%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20X%5Fg%5C%5C%0A%20%20%20%20Y%5Fg%5C%5C%0A%20%20%20%20Z%5Fg%5C%5C%0A%20%20%20%201%0A%20%20%20%20%5Cend%7Bbmatrix%7D%0A)
	///
	/// This problem is also known as solving the ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathbf%7BA%7D%5Cmathbf%7BX%7D%3D%5Cmathbf%7BZ%7D%5Cmathbf%7BB%7D) equation, with:
	///   - ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathbf%7BA%7D%20%5CLeftrightarrow%20%5Chspace%7B0%2E1em%7D%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Fw)
	///   - ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathbf%7BX%7D%20%5CLeftrightarrow%20%5Chspace%7B0%2E1em%7D%20%5F%7B%7D%5E%7Bw%7D%5Ctextrm%7BT%7D%5Fb)
	///   - ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathbf%7BZ%7D%20%5CLeftrightarrow%20%5Chspace%7B0%2E1em%7D%20%5F%7B%7D%5E%7Bc%7D%5Ctextrm%7BT%7D%5Fg)
	///   - ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathbf%7BB%7D%20%5CLeftrightarrow%20%5Chspace%7B0%2E1em%7D%20%5F%7B%7D%5E%7Bg%7D%5Ctextrm%7BT%7D%5Fb)
	///
	/// \note
	/// At least 3 measurements are required (input vectors size must be greater or equal to 3).
	///
	/// ## C++ default parameters
	/// * method: CALIB_ROBOT_WORLD_HAND_EYE_SHAH
	#[inline]
	pub fn calibrate_robot_world_hand_eye(r_world2cam: &impl ToInputArray, t_world2cam: &impl ToInputArray, r_base2gripper: &impl ToInputArray, t_base2gripper: &impl ToInputArray, r_base2world: &mut impl ToOutputArray, t_base2world: &mut impl ToOutputArray, r_gripper2cam: &mut impl ToOutputArray, t_gripper2cam: &mut impl ToOutputArray, method: crate::calib3d::RobotWorldHandEyeCalibrationMethod) -> Result<()> {
		input_array_arg!(r_world2cam);
		input_array_arg!(t_world2cam);
		input_array_arg!(r_base2gripper);
		input_array_arg!(t_base2gripper);
		output_array_arg!(r_base2world);
		output_array_arg!(t_base2world);
		output_array_arg!(r_gripper2cam);
		output_array_arg!(t_gripper2cam);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_calibrateRobotWorldHandEye_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_RobotWorldHandEyeCalibrationMethod(r_world2cam.as_raw__InputArray(), t_world2cam.as_raw__InputArray(), r_base2gripper.as_raw__InputArray(), t_base2gripper.as_raw__InputArray(), r_base2world.as_raw__OutputArray(), t_base2world.as_raw__OutputArray(), r_gripper2cam.as_raw__OutputArray(), t_gripper2cam.as_raw__OutputArray(), method, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes useful camera characteristics from the camera intrinsic matrix.
	///
	/// ## Parameters
	/// * cameraMatrix: Input camera intrinsic matrix that can be estimated by [calibrate_camera] or
	/// [stereo_calibrate] .
	/// * imageSize: Input image size in pixels.
	/// * apertureWidth: Physical width in mm of the sensor.
	/// * apertureHeight: Physical height in mm of the sensor.
	/// * fovx: Output field of view in degrees along the horizontal sensor axis.
	/// * fovy: Output field of view in degrees along the vertical sensor axis.
	/// * focalLength: Focal length of the lens in mm.
	/// * principalPoint: Principal point in mm.
	/// * aspectRatio: ![inline formula](https://latex.codecogs.com/png.latex?f%5Fy%2Ff%5Fx)
	///
	/// The function computes various useful camera characteristics from the previously estimated camera
	/// matrix.
	///
	///
	/// Note:
	///    Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for
	///    the chessboard pitch (it can thus be any value).
	#[inline]
	pub fn calibration_matrix_values(camera_matrix: &impl ToInputArray, image_size: core::Size, aperture_width: f64, aperture_height: f64, fovx: &mut f64, fovy: &mut f64, focal_length: &mut f64, principal_point: &mut core::Point2d, aspect_ratio: &mut f64) -> Result<()> {
		input_array_arg!(camera_matrix);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_calibrationMatrixValues_const__InputArrayR_Size_double_double_doubleR_doubleR_doubleR_Point2dR_doubleR(camera_matrix.as_raw__InputArray(), &image_size, aperture_width, aperture_height, fovx, fovy, focal_length, principal_point, aspect_ratio, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	#[inline]
	pub fn check_chessboard(img: &impl ToInputArray, size: core::Size) -> Result<bool> {
		input_array_arg!(img);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_checkChessboard_const__InputArrayR_Size(img.as_raw__InputArray(), &size, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Combines two rotation-and-shift transformations.
	///
	/// ## Parameters
	/// * rvec1: First rotation vector.
	/// * tvec1: First translation vector.
	/// * rvec2: Second rotation vector.
	/// * tvec2: Second translation vector.
	/// * rvec3: Output rotation vector of the superposition.
	/// * tvec3: Output translation vector of the superposition.
	/// * dr3dr1: Optional output derivative of rvec3 with regard to rvec1
	/// * dr3dt1: Optional output derivative of rvec3 with regard to tvec1
	/// * dr3dr2: Optional output derivative of rvec3 with regard to rvec2
	/// * dr3dt2: Optional output derivative of rvec3 with regard to tvec2
	/// * dt3dr1: Optional output derivative of tvec3 with regard to rvec1
	/// * dt3dt1: Optional output derivative of tvec3 with regard to tvec1
	/// * dt3dr2: Optional output derivative of tvec3 with regard to rvec2
	/// * dt3dt2: Optional output derivative of tvec3 with regard to tvec2
	///
	/// The functions compute:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20%5Ctexttt%7Brvec3%7D%20%3D%20%20%5Cmathrm%7Brodrigues%7D%20%5E%7B%2D1%7D%20%5Cleft%20%28%20%5Cmathrm%7Brodrigues%7D%20%28%20%5Ctexttt%7Brvec2%7D%20%29%20%20%5Ccdot%20%5Cmathrm%7Brodrigues%7D%20%28%20%5Ctexttt%7Brvec1%7D%20%29%20%5Cright%20%29%20%20%5C%5C%20%5Ctexttt%7Btvec3%7D%20%3D%20%20%5Cmathrm%7Brodrigues%7D%20%28%20%5Ctexttt%7Brvec2%7D%20%29%20%20%5Ccdot%20%5Ctexttt%7Btvec1%7D%20%2B%20%20%5Ctexttt%7Btvec2%7D%20%5Cend%7Barray%7D%20%2C)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathrm%7Brodrigues%7D) denotes a rotation vector to a rotation matrix transformation, and
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathrm%7Brodrigues%7D%5E%7B%2D1%7D) denotes the inverse transformation. See [rodrigues] for details.
	///
	/// Also, the functions can compute the derivatives of the output vectors with regards to the input
	/// vectors (see [mat_mul_deriv] ). The functions are used inside [stereo_calibrate] but can also be used in
	/// your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a
	/// function that contains a matrix multiplication.
	///
	/// ## Note
	/// This alternative version of [compose_rt] function uses the following default values for its arguments:
	/// * dr3dr1: noArray()
	/// * dr3dt1: noArray()
	/// * dr3dr2: noArray()
	/// * dr3dt2: noArray()
	/// * dt3dr1: noArray()
	/// * dt3dt1: noArray()
	/// * dt3dr2: noArray()
	/// * dt3dt2: noArray()
	#[inline]
	pub fn compose_rt_def(rvec1: &impl ToInputArray, tvec1: &impl ToInputArray, rvec2: &impl ToInputArray, tvec2: &impl ToInputArray, rvec3: &mut impl ToOutputArray, tvec3: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(rvec1);
		input_array_arg!(tvec1);
		input_array_arg!(rvec2);
		input_array_arg!(tvec2);
		output_array_arg!(rvec3);
		output_array_arg!(tvec3);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_composeRT_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(rvec1.as_raw__InputArray(), tvec1.as_raw__InputArray(), rvec2.as_raw__InputArray(), tvec2.as_raw__InputArray(), rvec3.as_raw__OutputArray(), tvec3.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Combines two rotation-and-shift transformations.
	///
	/// ## Parameters
	/// * rvec1: First rotation vector.
	/// * tvec1: First translation vector.
	/// * rvec2: Second rotation vector.
	/// * tvec2: Second translation vector.
	/// * rvec3: Output rotation vector of the superposition.
	/// * tvec3: Output translation vector of the superposition.
	/// * dr3dr1: Optional output derivative of rvec3 with regard to rvec1
	/// * dr3dt1: Optional output derivative of rvec3 with regard to tvec1
	/// * dr3dr2: Optional output derivative of rvec3 with regard to rvec2
	/// * dr3dt2: Optional output derivative of rvec3 with regard to tvec2
	/// * dt3dr1: Optional output derivative of tvec3 with regard to rvec1
	/// * dt3dt1: Optional output derivative of tvec3 with regard to tvec1
	/// * dt3dr2: Optional output derivative of tvec3 with regard to rvec2
	/// * dt3dt2: Optional output derivative of tvec3 with regard to tvec2
	///
	/// The functions compute:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20%5Ctexttt%7Brvec3%7D%20%3D%20%20%5Cmathrm%7Brodrigues%7D%20%5E%7B%2D1%7D%20%5Cleft%20%28%20%5Cmathrm%7Brodrigues%7D%20%28%20%5Ctexttt%7Brvec2%7D%20%29%20%20%5Ccdot%20%5Cmathrm%7Brodrigues%7D%20%28%20%5Ctexttt%7Brvec1%7D%20%29%20%5Cright%20%29%20%20%5C%5C%20%5Ctexttt%7Btvec3%7D%20%3D%20%20%5Cmathrm%7Brodrigues%7D%20%28%20%5Ctexttt%7Brvec2%7D%20%29%20%20%5Ccdot%20%5Ctexttt%7Btvec1%7D%20%2B%20%20%5Ctexttt%7Btvec2%7D%20%5Cend%7Barray%7D%20%2C)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathrm%7Brodrigues%7D) denotes a rotation vector to a rotation matrix transformation, and
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathrm%7Brodrigues%7D%5E%7B%2D1%7D) denotes the inverse transformation. See [rodrigues] for details.
	///
	/// Also, the functions can compute the derivatives of the output vectors with regards to the input
	/// vectors (see [mat_mul_deriv] ). The functions are used inside [stereo_calibrate] but can also be used in
	/// your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a
	/// function that contains a matrix multiplication.
	///
	/// ## C++ default parameters
	/// * dr3dr1: noArray()
	/// * dr3dt1: noArray()
	/// * dr3dr2: noArray()
	/// * dr3dt2: noArray()
	/// * dt3dr1: noArray()
	/// * dt3dt1: noArray()
	/// * dt3dr2: noArray()
	/// * dt3dt2: noArray()
	#[inline]
	pub fn compose_rt(rvec1: &impl ToInputArray, tvec1: &impl ToInputArray, rvec2: &impl ToInputArray, tvec2: &impl ToInputArray, rvec3: &mut impl ToOutputArray, tvec3: &mut impl ToOutputArray, dr3dr1: &mut impl ToOutputArray, dr3dt1: &mut impl ToOutputArray, dr3dr2: &mut impl ToOutputArray, dr3dt2: &mut impl ToOutputArray, dt3dr1: &mut impl ToOutputArray, dt3dt1: &mut impl ToOutputArray, dt3dr2: &mut impl ToOutputArray, dt3dt2: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(rvec1);
		input_array_arg!(tvec1);
		input_array_arg!(rvec2);
		input_array_arg!(tvec2);
		output_array_arg!(rvec3);
		output_array_arg!(tvec3);
		output_array_arg!(dr3dr1);
		output_array_arg!(dr3dt1);
		output_array_arg!(dr3dr2);
		output_array_arg!(dr3dt2);
		output_array_arg!(dt3dr1);
		output_array_arg!(dt3dt1);
		output_array_arg!(dt3dr2);
		output_array_arg!(dt3dt2);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_composeRT_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(rvec1.as_raw__InputArray(), tvec1.as_raw__InputArray(), rvec2.as_raw__InputArray(), tvec2.as_raw__InputArray(), rvec3.as_raw__OutputArray(), tvec3.as_raw__OutputArray(), dr3dr1.as_raw__OutputArray(), dr3dt1.as_raw__OutputArray(), dr3dr2.as_raw__OutputArray(), dr3dt2.as_raw__OutputArray(), dt3dr1.as_raw__OutputArray(), dt3dt1.as_raw__OutputArray(), dt3dr2.as_raw__OutputArray(), dt3dt2.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// For points in an image of a stereo pair, computes the corresponding epilines in the other image.
	///
	/// ## Parameters
	/// * points: Input points. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Ctimes%201) or ![inline formula](https://latex.codecogs.com/png.latex?1%20%5Ctimes%20N) matrix of type CV_32FC2 or
	/// vector\<Point2f\> .
	/// * whichImage: Index of the image (1 or 2) that contains the points .
	/// * F: Fundamental matrix that can be estimated using [find_fundamental_mat] or [stereo_rectify] .
	/// * lines: Output vector of the epipolar lines corresponding to the points in the other image.
	/// Each line ![inline formula](https://latex.codecogs.com/png.latex?ax%20%2B%20by%20%2B%20c%3D0) is encoded by 3 numbers ![inline formula](https://latex.codecogs.com/png.latex?%28a%2C%20b%2C%20c%29) .
	///
	/// For every point in one of the two images of a stereo pair, the function finds the equation of the
	/// corresponding epipolar line in the other image.
	///
	/// From the fundamental matrix definition (see [find_fundamental_mat] ), line ![inline formula](https://latex.codecogs.com/png.latex?l%5E%7B%282%29%7D%5Fi) in the second
	/// image for the point ![inline formula](https://latex.codecogs.com/png.latex?p%5E%7B%281%29%7D%5Fi) in the first image (when whichImage=1 ) is computed as:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?l%5E%7B%282%29%7D%5Fi%20%3D%20F%20p%5E%7B%281%29%7D%5Fi)
	///
	/// And vice versa, when whichImage=2, ![inline formula](https://latex.codecogs.com/png.latex?l%5E%7B%281%29%7D%5Fi) is computed from ![inline formula](https://latex.codecogs.com/png.latex?p%5E%7B%282%29%7D%5Fi) as:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?l%5E%7B%281%29%7D%5Fi%20%3D%20F%5ET%20p%5E%7B%282%29%7D%5Fi)
	///
	/// Line coefficients are defined up to a scale. They are normalized so that ![inline formula](https://latex.codecogs.com/png.latex?a%5Fi%5E2%2Bb%5Fi%5E2%3D1) .
	#[inline]
	pub fn compute_correspond_epilines(points: &impl ToInputArray, which_image: i32, f: &impl ToInputArray, lines: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(points);
		input_array_arg!(f);
		output_array_arg!(lines);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_computeCorrespondEpilines_const__InputArrayR_int_const__InputArrayR_const__OutputArrayR(points.as_raw__InputArray(), which_image, f.as_raw__InputArray(), lines.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Converts points from homogeneous to Euclidean space.
	///
	/// ## Parameters
	/// * src: Input vector of N-dimensional points.
	/// * dst: Output vector of N-1-dimensional points.
	///
	/// The function converts points homogeneous to Euclidean space using perspective projection. That is,
	/// each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the
	/// output point coordinates will be (0,0,0,...).
	#[inline]
	pub fn convert_points_from_homogeneous(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(src);
		output_array_arg!(dst);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_convertPointsFromHomogeneous_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Converts points to/from homogeneous coordinates.
	///
	/// ## Parameters
	/// * src: Input array or vector of 2D, 3D, or 4D points.
	/// * dst: Output vector of 2D, 3D, or 4D points.
	///
	/// The function converts 2D or 3D points from/to homogeneous coordinates by calling either
	/// [convert_points_to_homogeneous] or #convertPointsFromHomogeneous.
	///
	///
	/// Note: The function is obsolete. Use one of the previous two functions instead.
	#[inline]
	pub fn convert_points_homogeneous(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(src);
		output_array_arg!(dst);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_convertPointsHomogeneous_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Converts points from Euclidean to homogeneous space.
	///
	/// ## Parameters
	/// * src: Input vector of N-dimensional points.
	/// * dst: Output vector of N+1-dimensional points.
	///
	/// The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of
	/// point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1).
	#[inline]
	pub fn convert_points_to_homogeneous(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(src);
		output_array_arg!(dst);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_convertPointsToHomogeneous_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Refines coordinates of corresponding points.
	///
	/// ## Parameters
	/// * F: 3x3 fundamental matrix.
	/// * points1: 1xN array containing the first set of points.
	/// * points2: 1xN array containing the second set of points.
	/// * newPoints1: The optimized points1.
	/// * newPoints2: The optimized points2.
	///
	/// The function implements the Optimal Triangulation Method (see Multiple View Geometry [HartleyZ00](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_HartleyZ00) for details).
	/// For each given point correspondence points1[i] \<-\> points2[i], and a fundamental matrix F, it
	/// computes the corrected correspondences newPoints1[i] \<-\> newPoints2[i] that minimize the geometric
	/// error ![inline formula](https://latex.codecogs.com/png.latex?d%28points1%5Bi%5D%2C%20newPoints1%5Bi%5D%29%5E2%20%2B%20d%28points2%5Bi%5D%2CnewPoints2%5Bi%5D%29%5E2) (where ![inline formula](https://latex.codecogs.com/png.latex?d%28a%2Cb%29) is the
	/// geometric distance between points ![inline formula](https://latex.codecogs.com/png.latex?a) and ![inline formula](https://latex.codecogs.com/png.latex?b) ) subject to the epipolar constraint
	/// ![inline formula](https://latex.codecogs.com/png.latex?newPoints2%5ET%20%5Ccdot%20F%20%5Ccdot%20newPoints1%20%3D%200) .
	#[inline]
	pub fn correct_matches(f: &impl ToInputArray, points1: &impl ToInputArray, points2: &impl ToInputArray, new_points1: &mut impl ToOutputArray, new_points2: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(f);
		input_array_arg!(points1);
		input_array_arg!(points2);
		output_array_arg!(new_points1);
		output_array_arg!(new_points2);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_correctMatches_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(f.as_raw__InputArray(), points1.as_raw__InputArray(), points2.as_raw__InputArray(), new_points1.as_raw__OutputArray(), new_points2.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Decompose an essential matrix to possible rotations and translation.
	///
	/// ## Parameters
	/// * E: The input essential matrix.
	/// * R1: One possible rotation matrix.
	/// * R2: Another possible rotation matrix.
	/// * t: One possible translation.
	///
	/// This function decomposes the essential matrix E using svd decomposition [HartleyZ00](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_HartleyZ00). In
	/// general, four possible poses exist for the decomposition of E. They are ![inline formula](https://latex.codecogs.com/png.latex?%5BR%5F1%2C%20t%5D),
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5BR%5F1%2C%20%2Dt%5D), ![inline formula](https://latex.codecogs.com/png.latex?%5BR%5F2%2C%20t%5D), ![inline formula](https://latex.codecogs.com/png.latex?%5BR%5F2%2C%20%2Dt%5D).
	///
	/// If E gives the epipolar constraint ![inline formula](https://latex.codecogs.com/png.latex?%5Bp%5F2%3B%201%5D%5ET%20A%5E%7B%2DT%7D%20E%20A%5E%7B%2D1%7D%20%5Bp%5F1%3B%201%5D%20%3D%200) between the image
	/// points ![inline formula](https://latex.codecogs.com/png.latex?p%5F1) in the first image and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) in second image, then any of the tuples
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5BR%5F1%2C%20t%5D), ![inline formula](https://latex.codecogs.com/png.latex?%5BR%5F1%2C%20%2Dt%5D), ![inline formula](https://latex.codecogs.com/png.latex?%5BR%5F2%2C%20t%5D), ![inline formula](https://latex.codecogs.com/png.latex?%5BR%5F2%2C%20%2Dt%5D) is a change of basis from the first
	/// camera's coordinate system to the second camera's coordinate system. However, by decomposing E, one
	/// can only get the direction of the translation. For this reason, the translation t is returned with
	/// unit length.
	#[inline]
	pub fn decompose_essential_mat(e: &impl ToInputArray, r1: &mut impl ToOutputArray, r2: &mut impl ToOutputArray, t: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(e);
		output_array_arg!(r1);
		output_array_arg!(r2);
		output_array_arg!(t);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_decomposeEssentialMat_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(e.as_raw__InputArray(), r1.as_raw__OutputArray(), r2.as_raw__OutputArray(), t.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Decompose a homography matrix to rotation(s), translation(s) and plane normal(s).
	///
	/// ## Parameters
	/// * H: The input homography matrix between two images.
	/// * K: The input camera intrinsic matrix.
	/// * rotations: Array of rotation matrices.
	/// * translations: Array of translation matrices.
	/// * normals: Array of plane normal matrices.
	///
	/// This function extracts relative camera motion between two views of a planar object and returns up to
	/// four mathematical solution tuples of rotation, translation, and plane normal. The decomposition of
	/// the homography matrix H is described in detail in [Malis2007](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Malis2007).
	///
	/// If the homography H, induced by the plane, gives the constraint
	/// ![block formula](https://latex.codecogs.com/png.latex?s%5Fi%20%5Cbegin%7Bbmatrix%7D%20x%27%5Fi%5C%5C%20y%27%5Fi%5C%5C%201%20%5Cend%7Bbmatrix%7D%20%5Csim%20H%20%5Cbegin%7Bbmatrix%7D%20x%5Fi%5C%5C%20y%5Fi%5C%5C%201%20%5Cend%7Bbmatrix%7D) on the source image points
	/// ![inline formula](https://latex.codecogs.com/png.latex?p%5Fi) and the destination image points ![inline formula](https://latex.codecogs.com/png.latex?p%27%5Fi), then the tuple of rotations[k] and
	/// translations[k] is a change of basis from the source camera's coordinate system to the destination
	/// camera's coordinate system. However, by decomposing H, one can only get the translation normalized
	/// by the (typically unknown) depth of the scene, i.e. its direction but with normalized length.
	///
	/// If point correspondences are available, at least two solutions may further be invalidated, by
	/// applying positive depth constraint, i.e. all points must be in front of the camera.
	#[inline]
	pub fn decompose_homography_mat(h: &impl ToInputArray, k: &impl ToInputArray, rotations: &mut impl ToOutputArray, translations: &mut impl ToOutputArray, normals: &mut impl ToOutputArray) -> Result<i32> {
		input_array_arg!(h);
		input_array_arg!(k);
		output_array_arg!(rotations);
		output_array_arg!(translations);
		output_array_arg!(normals);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_decomposeHomographyMat_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(h.as_raw__InputArray(), k.as_raw__InputArray(), rotations.as_raw__OutputArray(), translations.as_raw__OutputArray(), normals.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
	///
	/// ## Parameters
	/// * projMatrix: 3x4 input projection matrix P.
	/// * cameraMatrix: Output 3x3 camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D).
	/// * rotMatrix: Output 3x3 external rotation matrix R.
	/// * transVect: Output 4x1 translation vector T.
	/// * rotMatrixX: Optional 3x3 rotation matrix around x-axis.
	/// * rotMatrixY: Optional 3x3 rotation matrix around y-axis.
	/// * rotMatrixZ: Optional 3x3 rotation matrix around z-axis.
	/// * eulerAngles: Optional three-element vector containing three Euler angles of rotation in
	/// degrees.
	///
	/// The function computes a decomposition of a projection matrix into a calibration and a rotation
	/// matrix and the position of a camera.
	///
	/// It optionally returns three rotation matrices, one for each axis, and three Euler angles that could
	/// be used in OpenGL. Note, there is always more than one sequence of rotations about the three
	/// principal axes that results in the same orientation of an object, e.g. see [Slabaugh](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Slabaugh) . Returned
	/// three rotation matrices and corresponding three Euler angles are only one of the possible solutions.
	///
	/// The function is based on [rq_decomp3x3] .
	///
	/// ## Note
	/// This alternative version of [decompose_projection_matrix] function uses the following default values for its arguments:
	/// * rot_matrix_x: noArray()
	/// * rot_matrix_y: noArray()
	/// * rot_matrix_z: noArray()
	/// * euler_angles: noArray()
	#[inline]
	pub fn decompose_projection_matrix_def(proj_matrix: &impl ToInputArray, camera_matrix: &mut impl ToOutputArray, rot_matrix: &mut impl ToOutputArray, trans_vect: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(proj_matrix);
		output_array_arg!(camera_matrix);
		output_array_arg!(rot_matrix);
		output_array_arg!(trans_vect);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_decomposeProjectionMatrix_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(proj_matrix.as_raw__InputArray(), camera_matrix.as_raw__OutputArray(), rot_matrix.as_raw__OutputArray(), trans_vect.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
	///
	/// ## Parameters
	/// * projMatrix: 3x4 input projection matrix P.
	/// * cameraMatrix: Output 3x3 camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D).
	/// * rotMatrix: Output 3x3 external rotation matrix R.
	/// * transVect: Output 4x1 translation vector T.
	/// * rotMatrixX: Optional 3x3 rotation matrix around x-axis.
	/// * rotMatrixY: Optional 3x3 rotation matrix around y-axis.
	/// * rotMatrixZ: Optional 3x3 rotation matrix around z-axis.
	/// * eulerAngles: Optional three-element vector containing three Euler angles of rotation in
	/// degrees.
	///
	/// The function computes a decomposition of a projection matrix into a calibration and a rotation
	/// matrix and the position of a camera.
	///
	/// It optionally returns three rotation matrices, one for each axis, and three Euler angles that could
	/// be used in OpenGL. Note, there is always more than one sequence of rotations about the three
	/// principal axes that results in the same orientation of an object, e.g. see [Slabaugh](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Slabaugh) . Returned
	/// three rotation matrices and corresponding three Euler angles are only one of the possible solutions.
	///
	/// The function is based on [rq_decomp3x3] .
	///
	/// ## C++ default parameters
	/// * rot_matrix_x: noArray()
	/// * rot_matrix_y: noArray()
	/// * rot_matrix_z: noArray()
	/// * euler_angles: noArray()
	#[inline]
	pub fn decompose_projection_matrix(proj_matrix: &impl ToInputArray, camera_matrix: &mut impl ToOutputArray, rot_matrix: &mut impl ToOutputArray, trans_vect: &mut impl ToOutputArray, rot_matrix_x: &mut impl ToOutputArray, rot_matrix_y: &mut impl ToOutputArray, rot_matrix_z: &mut impl ToOutputArray, euler_angles: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(proj_matrix);
		output_array_arg!(camera_matrix);
		output_array_arg!(rot_matrix);
		output_array_arg!(trans_vect);
		output_array_arg!(rot_matrix_x);
		output_array_arg!(rot_matrix_y);
		output_array_arg!(rot_matrix_z);
		output_array_arg!(euler_angles);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_decomposeProjectionMatrix_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(proj_matrix.as_raw__InputArray(), camera_matrix.as_raw__OutputArray(), rot_matrix.as_raw__OutputArray(), trans_vect.as_raw__OutputArray(), rot_matrix_x.as_raw__OutputArray(), rot_matrix_y.as_raw__OutputArray(), rot_matrix_z.as_raw__OutputArray(), euler_angles.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Renders the detected chessboard corners.
	///
	/// ## Parameters
	/// * image: Destination image. It must be an 8-bit color image.
	/// * patternSize: Number of inner corners per a chessboard row and column
	/// (patternSize = cv::Size(points_per_row,points_per_column)).
	/// * corners: Array of detected corners, the output of #findChessboardCorners.
	/// * patternWasFound: Parameter indicating whether the complete board was found or not. The
	/// return value of [find_chessboard_corners] should be passed here.
	///
	/// The function draws individual chessboard corners detected either as red circles if the board was not
	/// found, or as colored corners connected with lines if the board was found.
	#[inline]
	pub fn draw_chessboard_corners(image: &mut impl ToInputOutputArray, pattern_size: core::Size, corners: &impl ToInputArray, pattern_was_found: bool) -> Result<()> {
		input_output_array_arg!(image);
		input_array_arg!(corners);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_drawChessboardCorners_const__InputOutputArrayR_Size_const__InputArrayR_bool(image.as_raw__InputOutputArray(), &pattern_size, corners.as_raw__InputArray(), pattern_was_found, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Draw axes of the world/object coordinate system from pose estimation. see also: solvePnP
	///
	/// ## Parameters
	/// * image: Input/output image. It must have 1 or 3 channels. The number of channels is not altered.
	/// * cameraMatrix: Input 3x3 floating-point matrix of camera intrinsic parameters.
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D)
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs). If the vector is empty, the zero distortion coefficients are assumed.
	/// * rvec: Rotation vector (see [Rodrigues] ) that, together with tvec, brings points from
	/// the model coordinate system to the camera coordinate system.
	/// * tvec: Translation vector.
	/// * length: Length of the painted axes in the same unit than tvec (usually in meters).
	/// * thickness: Line thickness of the painted axes.
	///
	/// This function draws the axes of the world/object coordinate system w.r.t. to the camera frame.
	/// OX is drawn in red, OY in green and OZ in blue.
	///
	/// ## Note
	/// This alternative version of [draw_frame_axes] function uses the following default values for its arguments:
	/// * thickness: 3
	#[inline]
	pub fn draw_frame_axes_def(image: &mut impl ToInputOutputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvec: &impl ToInputArray, tvec: &impl ToInputArray, length: f32) -> Result<()> {
		input_output_array_arg!(image);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		input_array_arg!(rvec);
		input_array_arg!(tvec);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_drawFrameAxes_const__InputOutputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_float(image.as_raw__InputOutputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__InputArray(), tvec.as_raw__InputArray(), length, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Draw axes of the world/object coordinate system from pose estimation. see also: solvePnP
	///
	/// ## Parameters
	/// * image: Input/output image. It must have 1 or 3 channels. The number of channels is not altered.
	/// * cameraMatrix: Input 3x3 floating-point matrix of camera intrinsic parameters.
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D)
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs). If the vector is empty, the zero distortion coefficients are assumed.
	/// * rvec: Rotation vector (see [Rodrigues] ) that, together with tvec, brings points from
	/// the model coordinate system to the camera coordinate system.
	/// * tvec: Translation vector.
	/// * length: Length of the painted axes in the same unit than tvec (usually in meters).
	/// * thickness: Line thickness of the painted axes.
	///
	/// This function draws the axes of the world/object coordinate system w.r.t. to the camera frame.
	/// OX is drawn in red, OY in green and OZ in blue.
	///
	/// ## C++ default parameters
	/// * thickness: 3
	#[inline]
	pub fn draw_frame_axes(image: &mut impl ToInputOutputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvec: &impl ToInputArray, tvec: &impl ToInputArray, length: f32, thickness: i32) -> Result<()> {
		input_output_array_arg!(image);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		input_array_arg!(rvec);
		input_array_arg!(tvec);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_drawFrameAxes_const__InputOutputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_float_int(image.as_raw__InputOutputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__InputArray(), tvec.as_raw__InputArray(), length, thickness, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes an optimal affine transformation between two 2D point sets.
	///
	/// It computes
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Bbmatrix%7D%0Ax%5C%5C%0Ay%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%3D%0A%5Cbegin%7Bbmatrix%7D%0Aa%5F%7B11%7D%20%26%20a%5F%7B12%7D%5C%5C%0Aa%5F%7B21%7D%20%26%20a%5F%7B22%7D%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%5Cbegin%7Bbmatrix%7D%0AX%5C%5C%0AY%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%2B%0A%5Cbegin%7Bbmatrix%7D%0Ab%5F1%5C%5C%0Ab%5F2%5C%5C%0A%5Cend%7Bbmatrix%7D%0A)
	///
	/// ## Parameters
	/// * from: First input 2D point set containing ![inline formula](https://latex.codecogs.com/png.latex?%28X%2CY%29).
	/// * to: Second input 2D point set containing ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%29).
	/// * inliers: Output vector indicating which points are inliers (1-inlier, 0-outlier).
	/// * method: Robust method used to compute transformation. The following methods are possible:
	/// *   [RANSAC] - RANSAC-based robust method
	/// *   [LMEDS] - Least-Median robust method
	/// RANSAC is the default method.
	/// * ransacReprojThreshold: Maximum reprojection error in the RANSAC algorithm to consider
	/// a point as an inlier. Applies only to RANSAC.
	/// * maxIters: The maximum number of robust method iterations.
	/// * confidence: Confidence level, between 0 and 1, for the estimated transformation. Anything
	/// between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
	/// significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
	/// * refineIters: Maximum number of iterations of refining algorithm (Levenberg-Marquardt).
	/// Passing 0 will disable refining, so the output matrix will be output of robust method.
	///
	/// ## Returns
	/// Output 2D affine transformation matrix ![inline formula](https://latex.codecogs.com/png.latex?2%20%5Ctimes%203) or empty matrix if transformation
	/// could not be estimated. The returned matrix has the following form:
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Bbmatrix%7D%0Aa%5F%7B11%7D%20%26%20a%5F%7B12%7D%20%26%20b%5F1%5C%5C%0Aa%5F%7B21%7D%20%26%20a%5F%7B22%7D%20%26%20b%5F2%5C%5C%0A%5Cend%7Bbmatrix%7D%0A)
	///
	/// The function estimates an optimal 2D affine transformation between two 2D point sets using the
	/// selected robust algorithm.
	///
	/// The computed transformation is then refined further (using only inliers) with the
	/// Levenberg-Marquardt method to reduce the re-projection error even more.
	///
	///
	/// Note:
	/// The RANSAC method can handle practically any ratio of outliers but needs a threshold to
	/// distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
	/// correctly only when there are more than 50% of inliers.
	/// ## See also
	/// estimateAffinePartial2D, getAffineTransform
	///
	/// ## Note
	/// This alternative version of [estimate_affine_2d] function uses the following default values for its arguments:
	/// * inliers: noArray()
	/// * method: RANSAC
	/// * ransac_reproj_threshold: 3
	/// * max_iters: 2000
	/// * confidence: 0.99
	/// * refine_iters: 10
	#[inline]
	pub fn estimate_affine_2d_def(from: &impl ToInputArray, to: &impl ToInputArray) -> Result<core::Mat> {
		input_array_arg!(from);
		input_array_arg!(to);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_estimateAffine2D_const__InputArrayR_const__InputArrayR(from.as_raw__InputArray(), to.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	#[inline]
	pub fn estimate_affine_2d_1(pts1: &impl ToInputArray, pts2: &impl ToInputArray, inliers: &mut impl ToOutputArray, params: crate::calib3d::UsacParams) -> Result<core::Mat> {
		input_array_arg!(pts1);
		input_array_arg!(pts2);
		output_array_arg!(inliers);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_estimateAffine2D_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const_UsacParamsR(pts1.as_raw__InputArray(), pts2.as_raw__InputArray(), inliers.as_raw__OutputArray(), &params, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Computes an optimal affine transformation between two 2D point sets.
	///
	/// It computes
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Bbmatrix%7D%0Ax%5C%5C%0Ay%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%3D%0A%5Cbegin%7Bbmatrix%7D%0Aa%5F%7B11%7D%20%26%20a%5F%7B12%7D%5C%5C%0Aa%5F%7B21%7D%20%26%20a%5F%7B22%7D%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%5Cbegin%7Bbmatrix%7D%0AX%5C%5C%0AY%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%2B%0A%5Cbegin%7Bbmatrix%7D%0Ab%5F1%5C%5C%0Ab%5F2%5C%5C%0A%5Cend%7Bbmatrix%7D%0A)
	///
	/// ## Parameters
	/// * from: First input 2D point set containing ![inline formula](https://latex.codecogs.com/png.latex?%28X%2CY%29).
	/// * to: Second input 2D point set containing ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%29).
	/// * inliers: Output vector indicating which points are inliers (1-inlier, 0-outlier).
	/// * method: Robust method used to compute transformation. The following methods are possible:
	/// *   [RANSAC] - RANSAC-based robust method
	/// *   [LMEDS] - Least-Median robust method
	/// RANSAC is the default method.
	/// * ransacReprojThreshold: Maximum reprojection error in the RANSAC algorithm to consider
	/// a point as an inlier. Applies only to RANSAC.
	/// * maxIters: The maximum number of robust method iterations.
	/// * confidence: Confidence level, between 0 and 1, for the estimated transformation. Anything
	/// between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
	/// significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
	/// * refineIters: Maximum number of iterations of refining algorithm (Levenberg-Marquardt).
	/// Passing 0 will disable refining, so the output matrix will be output of robust method.
	///
	/// ## Returns
	/// Output 2D affine transformation matrix ![inline formula](https://latex.codecogs.com/png.latex?2%20%5Ctimes%203) or empty matrix if transformation
	/// could not be estimated. The returned matrix has the following form:
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Bbmatrix%7D%0Aa%5F%7B11%7D%20%26%20a%5F%7B12%7D%20%26%20b%5F1%5C%5C%0Aa%5F%7B21%7D%20%26%20a%5F%7B22%7D%20%26%20b%5F2%5C%5C%0A%5Cend%7Bbmatrix%7D%0A)
	///
	/// The function estimates an optimal 2D affine transformation between two 2D point sets using the
	/// selected robust algorithm.
	///
	/// The computed transformation is then refined further (using only inliers) with the
	/// Levenberg-Marquardt method to reduce the re-projection error even more.
	///
	///
	/// Note:
	/// The RANSAC method can handle practically any ratio of outliers but needs a threshold to
	/// distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
	/// correctly only when there are more than 50% of inliers.
	/// ## See also
	/// estimateAffinePartial2D, getAffineTransform
	///
	/// ## C++ default parameters
	/// * inliers: noArray()
	/// * method: RANSAC
	/// * ransac_reproj_threshold: 3
	/// * max_iters: 2000
	/// * confidence: 0.99
	/// * refine_iters: 10
	#[inline]
	pub fn estimate_affine_2d(from: &impl ToInputArray, to: &impl ToInputArray, inliers: &mut impl ToOutputArray, method: i32, ransac_reproj_threshold: f64, max_iters: size_t, confidence: f64, refine_iters: size_t) -> Result<core::Mat> {
		input_array_arg!(from);
		input_array_arg!(to);
		output_array_arg!(inliers);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_estimateAffine2D_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_double_size_t_double_size_t(from.as_raw__InputArray(), to.as_raw__InputArray(), inliers.as_raw__OutputArray(), method, ransac_reproj_threshold, max_iters, confidence, refine_iters, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Computes an optimal affine transformation between two 3D point sets.
	///
	/// It computes ![inline formula](https://latex.codecogs.com/png.latex?R%2Cs%2Ct) minimizing ![inline formula](https://latex.codecogs.com/png.latex?%5Csum%7Bi%7D%20dst%5Fi%20%2D%20c%20%5Ccdot%20R%20%5Ccdot%20src%5Fi%20)
	/// where ![inline formula](https://latex.codecogs.com/png.latex?R) is a 3x3 rotation matrix, ![inline formula](https://latex.codecogs.com/png.latex?t) is a 3x1 translation vector and ![inline formula](https://latex.codecogs.com/png.latex?s) is a
	/// scalar size value. This is an implementation of the algorithm by Umeyama \cite umeyama1991least .
	/// The estimated affine transform has a homogeneous scale which is a subclass of affine
	/// transformations with 7 degrees of freedom. The paired point sets need to comprise at least 3
	/// points each.
	///
	/// ## Parameters
	/// * src: First input 3D point set.
	/// * dst: Second input 3D point set.
	/// * scale: If null is passed, the scale parameter c will be assumed to be 1.0.
	/// Else the pointed-to variable will be set to the optimal scale.
	/// * force_rotation: If true, the returned rotation will never be a reflection.
	/// This might be unwanted, e.g. when optimizing a transform between a right- and a
	/// left-handed coordinate system.
	/// ## Returns
	/// 3D affine transformation matrix ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%204) of the form
	/// ![block formula](https://latex.codecogs.com/png.latex?T%20%3D%0A%5Cbegin%7Bbmatrix%7D%0AR%20%26%20t%5C%5C%0A%5Cend%7Bbmatrix%7D%0A)
	///
	/// ## Note
	/// This alternative version of [estimate_affine_3d_1] function uses the following default values for its arguments:
	/// * scale: nullptr
	/// * force_rotation: true
	#[inline]
	pub fn estimate_affine_3d_1_def(src: &impl ToInputArray, dst: &impl ToInputArray) -> Result<core::Mat> {
		input_array_arg!(src);
		input_array_arg!(dst);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_estimateAffine3D_const__InputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Computes an optimal affine transformation between two 3D point sets.
	///
	/// It computes
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Bbmatrix%7D%0Ax%5C%5C%0Ay%5C%5C%0Az%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%3D%0A%5Cbegin%7Bbmatrix%7D%0Aa%5F%7B11%7D%20%26%20a%5F%7B12%7D%20%26%20a%5F%7B13%7D%5C%5C%0Aa%5F%7B21%7D%20%26%20a%5F%7B22%7D%20%26%20a%5F%7B23%7D%5C%5C%0Aa%5F%7B31%7D%20%26%20a%5F%7B32%7D%20%26%20a%5F%7B33%7D%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%5Cbegin%7Bbmatrix%7D%0AX%5C%5C%0AY%5C%5C%0AZ%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%2B%0A%5Cbegin%7Bbmatrix%7D%0Ab%5F1%5C%5C%0Ab%5F2%5C%5C%0Ab%5F3%5C%5C%0A%5Cend%7Bbmatrix%7D%0A)
	///
	/// ## Parameters
	/// * src: First input 3D point set containing ![inline formula](https://latex.codecogs.com/png.latex?%28X%2CY%2CZ%29).
	/// * dst: Second input 3D point set containing ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%2Cz%29).
	/// * out: Output 3D affine transformation matrix ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%204) of the form
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Bbmatrix%7D%0Aa%5F%7B11%7D%20%26%20a%5F%7B12%7D%20%26%20a%5F%7B13%7D%20%26%20b%5F1%5C%5C%0Aa%5F%7B21%7D%20%26%20a%5F%7B22%7D%20%26%20a%5F%7B23%7D%20%26%20b%5F2%5C%5C%0Aa%5F%7B31%7D%20%26%20a%5F%7B32%7D%20%26%20a%5F%7B33%7D%20%26%20b%5F3%5C%5C%0A%5Cend%7Bbmatrix%7D%0A)
	/// * inliers: Output vector indicating which points are inliers (1-inlier, 0-outlier).
	/// * ransacThreshold: Maximum reprojection error in the RANSAC algorithm to consider a point as
	/// an inlier.
	/// * confidence: Confidence level, between 0 and 1, for the estimated transformation. Anything
	/// between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
	/// significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
	///
	/// The function estimates an optimal 3D affine transformation between two 3D point sets using the
	/// RANSAC algorithm.
	///
	/// ## Note
	/// This alternative version of [estimate_affine_3d] function uses the following default values for its arguments:
	/// * ransac_threshold: 3
	/// * confidence: 0.99
	#[inline]
	pub fn estimate_affine_3d_def(src: &impl ToInputArray, dst: &impl ToInputArray, out: &mut impl ToOutputArray, inliers: &mut impl ToOutputArray) -> Result<i32> {
		input_array_arg!(src);
		input_array_arg!(dst);
		output_array_arg!(out);
		output_array_arg!(inliers);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_estimateAffine3D_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__InputArray(), out.as_raw__OutputArray(), inliers.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes an optimal affine transformation between two 3D point sets.
	///
	/// It computes
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Bbmatrix%7D%0Ax%5C%5C%0Ay%5C%5C%0Az%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%3D%0A%5Cbegin%7Bbmatrix%7D%0Aa%5F%7B11%7D%20%26%20a%5F%7B12%7D%20%26%20a%5F%7B13%7D%5C%5C%0Aa%5F%7B21%7D%20%26%20a%5F%7B22%7D%20%26%20a%5F%7B23%7D%5C%5C%0Aa%5F%7B31%7D%20%26%20a%5F%7B32%7D%20%26%20a%5F%7B33%7D%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%5Cbegin%7Bbmatrix%7D%0AX%5C%5C%0AY%5C%5C%0AZ%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%2B%0A%5Cbegin%7Bbmatrix%7D%0Ab%5F1%5C%5C%0Ab%5F2%5C%5C%0Ab%5F3%5C%5C%0A%5Cend%7Bbmatrix%7D%0A)
	///
	/// ## Parameters
	/// * src: First input 3D point set containing ![inline formula](https://latex.codecogs.com/png.latex?%28X%2CY%2CZ%29).
	/// * dst: Second input 3D point set containing ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%2Cz%29).
	/// * out: Output 3D affine transformation matrix ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%204) of the form
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Bbmatrix%7D%0Aa%5F%7B11%7D%20%26%20a%5F%7B12%7D%20%26%20a%5F%7B13%7D%20%26%20b%5F1%5C%5C%0Aa%5F%7B21%7D%20%26%20a%5F%7B22%7D%20%26%20a%5F%7B23%7D%20%26%20b%5F2%5C%5C%0Aa%5F%7B31%7D%20%26%20a%5F%7B32%7D%20%26%20a%5F%7B33%7D%20%26%20b%5F3%5C%5C%0A%5Cend%7Bbmatrix%7D%0A)
	/// * inliers: Output vector indicating which points are inliers (1-inlier, 0-outlier).
	/// * ransacThreshold: Maximum reprojection error in the RANSAC algorithm to consider a point as
	/// an inlier.
	/// * confidence: Confidence level, between 0 and 1, for the estimated transformation. Anything
	/// between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
	/// significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
	///
	/// The function estimates an optimal 3D affine transformation between two 3D point sets using the
	/// RANSAC algorithm.
	///
	/// ## C++ default parameters
	/// * ransac_threshold: 3
	/// * confidence: 0.99
	#[inline]
	pub fn estimate_affine_3d(src: &impl ToInputArray, dst: &impl ToInputArray, out: &mut impl ToOutputArray, inliers: &mut impl ToOutputArray, ransac_threshold: f64, confidence: f64) -> Result<i32> {
		input_array_arg!(src);
		input_array_arg!(dst);
		output_array_arg!(out);
		output_array_arg!(inliers);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_estimateAffine3D_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_double_double(src.as_raw__InputArray(), dst.as_raw__InputArray(), out.as_raw__OutputArray(), inliers.as_raw__OutputArray(), ransac_threshold, confidence, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes an optimal affine transformation between two 3D point sets.
	///
	/// It computes ![inline formula](https://latex.codecogs.com/png.latex?R%2Cs%2Ct) minimizing ![inline formula](https://latex.codecogs.com/png.latex?%5Csum%7Bi%7D%20dst%5Fi%20%2D%20c%20%5Ccdot%20R%20%5Ccdot%20src%5Fi%20)
	/// where ![inline formula](https://latex.codecogs.com/png.latex?R) is a 3x3 rotation matrix, ![inline formula](https://latex.codecogs.com/png.latex?t) is a 3x1 translation vector and ![inline formula](https://latex.codecogs.com/png.latex?s) is a
	/// scalar size value. This is an implementation of the algorithm by Umeyama \cite umeyama1991least .
	/// The estimated affine transform has a homogeneous scale which is a subclass of affine
	/// transformations with 7 degrees of freedom. The paired point sets need to comprise at least 3
	/// points each.
	///
	/// ## Parameters
	/// * src: First input 3D point set.
	/// * dst: Second input 3D point set.
	/// * scale: If null is passed, the scale parameter c will be assumed to be 1.0.
	/// Else the pointed-to variable will be set to the optimal scale.
	/// * force_rotation: If true, the returned rotation will never be a reflection.
	/// This might be unwanted, e.g. when optimizing a transform between a right- and a
	/// left-handed coordinate system.
	/// ## Returns
	/// 3D affine transformation matrix ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%204) of the form
	/// ![block formula](https://latex.codecogs.com/png.latex?T%20%3D%0A%5Cbegin%7Bbmatrix%7D%0AR%20%26%20t%5C%5C%0A%5Cend%7Bbmatrix%7D%0A)
	///
	/// ## C++ default parameters
	/// * scale: nullptr
	/// * force_rotation: true
	#[inline]
	pub fn estimate_affine_3d_1(src: &impl ToInputArray, dst: &impl ToInputArray, scale: &mut f64, force_rotation: bool) -> Result<core::Mat> {
		input_array_arg!(src);
		input_array_arg!(dst);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_estimateAffine3D_const__InputArrayR_const__InputArrayR_doubleX_bool(src.as_raw__InputArray(), dst.as_raw__InputArray(), scale, force_rotation, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Computes an optimal limited affine transformation with 4 degrees of freedom between
	/// two 2D point sets.
	///
	/// ## Parameters
	/// * from: First input 2D point set.
	/// * to: Second input 2D point set.
	/// * inliers: Output vector indicating which points are inliers.
	/// * method: Robust method used to compute transformation. The following methods are possible:
	/// *   [RANSAC] - RANSAC-based robust method
	/// *   [LMEDS] - Least-Median robust method
	/// RANSAC is the default method.
	/// * ransacReprojThreshold: Maximum reprojection error in the RANSAC algorithm to consider
	/// a point as an inlier. Applies only to RANSAC.
	/// * maxIters: The maximum number of robust method iterations.
	/// * confidence: Confidence level, between 0 and 1, for the estimated transformation. Anything
	/// between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
	/// significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
	/// * refineIters: Maximum number of iterations of refining algorithm (Levenberg-Marquardt).
	/// Passing 0 will disable refining, so the output matrix will be output of robust method.
	///
	/// ## Returns
	/// Output 2D affine transformation (4 degrees of freedom) matrix ![inline formula](https://latex.codecogs.com/png.latex?2%20%5Ctimes%203) or
	/// empty matrix if transformation could not be estimated.
	///
	/// The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to
	/// combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust
	/// estimation.
	///
	/// The computed transformation is then refined further (using only inliers) with the
	/// Levenberg-Marquardt method to reduce the re-projection error even more.
	///
	/// Estimated transformation matrix is:
	/// ![block formula](https://latex.codecogs.com/png.latex?%20%5Cbegin%7Bbmatrix%7D%20%5Ccos%28%5Ctheta%29%20%5Ccdot%20s%20%26%20%2D%5Csin%28%5Ctheta%29%20%5Ccdot%20s%20%26%20t%5Fx%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5Csin%28%5Ctheta%29%20%5Ccdot%20s%20%26%20%5Ccos%28%5Ctheta%29%20%5Ccdot%20s%20%26%20t%5Fy%0A%5Cend%7Bbmatrix%7D%20)
	/// Where ![inline formula](https://latex.codecogs.com/png.latex?%20%5Ctheta%20) is the rotation angle, ![inline formula](https://latex.codecogs.com/png.latex?%20s%20) the scaling factor and ![inline formula](https://latex.codecogs.com/png.latex?%20t%5Fx%2C%20t%5Fy%20) are
	/// translations in ![inline formula](https://latex.codecogs.com/png.latex?%20x%2C%20y%20) axes respectively.
	///
	///
	/// Note:
	/// The RANSAC method can handle practically any ratio of outliers but need a threshold to
	/// distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
	/// correctly only when there are more than 50% of inliers.
	/// ## See also
	/// estimateAffine2D, getAffineTransform
	///
	/// ## Note
	/// This alternative version of [estimate_affine_partial_2d] function uses the following default values for its arguments:
	/// * inliers: noArray()
	/// * method: RANSAC
	/// * ransac_reproj_threshold: 3
	/// * max_iters: 2000
	/// * confidence: 0.99
	/// * refine_iters: 10
	#[inline]
	pub fn estimate_affine_partial_2d_def(from: &impl ToInputArray, to: &impl ToInputArray) -> Result<core::Mat> {
		input_array_arg!(from);
		input_array_arg!(to);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_estimateAffinePartial2D_const__InputArrayR_const__InputArrayR(from.as_raw__InputArray(), to.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Computes an optimal limited affine transformation with 4 degrees of freedom between
	/// two 2D point sets.
	///
	/// ## Parameters
	/// * from: First input 2D point set.
	/// * to: Second input 2D point set.
	/// * inliers: Output vector indicating which points are inliers.
	/// * method: Robust method used to compute transformation. The following methods are possible:
	/// *   [RANSAC] - RANSAC-based robust method
	/// *   [LMEDS] - Least-Median robust method
	/// RANSAC is the default method.
	/// * ransacReprojThreshold: Maximum reprojection error in the RANSAC algorithm to consider
	/// a point as an inlier. Applies only to RANSAC.
	/// * maxIters: The maximum number of robust method iterations.
	/// * confidence: Confidence level, between 0 and 1, for the estimated transformation. Anything
	/// between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
	/// significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
	/// * refineIters: Maximum number of iterations of refining algorithm (Levenberg-Marquardt).
	/// Passing 0 will disable refining, so the output matrix will be output of robust method.
	///
	/// ## Returns
	/// Output 2D affine transformation (4 degrees of freedom) matrix ![inline formula](https://latex.codecogs.com/png.latex?2%20%5Ctimes%203) or
	/// empty matrix if transformation could not be estimated.
	///
	/// The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to
	/// combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust
	/// estimation.
	///
	/// The computed transformation is then refined further (using only inliers) with the
	/// Levenberg-Marquardt method to reduce the re-projection error even more.
	///
	/// Estimated transformation matrix is:
	/// ![block formula](https://latex.codecogs.com/png.latex?%20%5Cbegin%7Bbmatrix%7D%20%5Ccos%28%5Ctheta%29%20%5Ccdot%20s%20%26%20%2D%5Csin%28%5Ctheta%29%20%5Ccdot%20s%20%26%20t%5Fx%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5Csin%28%5Ctheta%29%20%5Ccdot%20s%20%26%20%5Ccos%28%5Ctheta%29%20%5Ccdot%20s%20%26%20t%5Fy%0A%5Cend%7Bbmatrix%7D%20)
	/// Where ![inline formula](https://latex.codecogs.com/png.latex?%20%5Ctheta%20) is the rotation angle, ![inline formula](https://latex.codecogs.com/png.latex?%20s%20) the scaling factor and ![inline formula](https://latex.codecogs.com/png.latex?%20t%5Fx%2C%20t%5Fy%20) are
	/// translations in ![inline formula](https://latex.codecogs.com/png.latex?%20x%2C%20y%20) axes respectively.
	///
	///
	/// Note:
	/// The RANSAC method can handle practically any ratio of outliers but need a threshold to
	/// distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
	/// correctly only when there are more than 50% of inliers.
	/// ## See also
	/// estimateAffine2D, getAffineTransform
	///
	/// ## C++ default parameters
	/// * inliers: noArray()
	/// * method: RANSAC
	/// * ransac_reproj_threshold: 3
	/// * max_iters: 2000
	/// * confidence: 0.99
	/// * refine_iters: 10
	#[inline]
	pub fn estimate_affine_partial_2d(from: &impl ToInputArray, to: &impl ToInputArray, inliers: &mut impl ToOutputArray, method: i32, ransac_reproj_threshold: f64, max_iters: size_t, confidence: f64, refine_iters: size_t) -> Result<core::Mat> {
		input_array_arg!(from);
		input_array_arg!(to);
		output_array_arg!(inliers);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_estimateAffinePartial2D_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_double_size_t_double_size_t(from.as_raw__InputArray(), to.as_raw__InputArray(), inliers.as_raw__OutputArray(), method, ransac_reproj_threshold, max_iters, confidence, refine_iters, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Estimates the sharpness of a detected chessboard.
	///
	/// Image sharpness, as well as brightness, are a critical parameter for accuracte
	/// camera calibration. For accessing these parameters for filtering out
	/// problematic calibraiton images, this method calculates edge profiles by traveling from
	/// black to white chessboard cell centers. Based on this, the number of pixels is
	/// calculated required to transit from black to white. This width of the
	/// transition area is a good indication of how sharp the chessboard is imaged
	/// and should be below ~3.0 pixels.
	///
	/// ## Parameters
	/// * image: Gray image used to find chessboard corners
	/// * patternSize: Size of a found chessboard pattern
	/// * corners: Corners found by [find_chessboard_corners_sb]
	/// * rise_distance: Rise distance 0.8 means 10% ... 90% of the final signal strength
	/// * vertical: By default edge responses for horizontal lines are calculated
	/// * sharpness: Optional output array with a sharpness value for calculated edge responses (see description)
	///
	/// The optional sharpness array is of type CV_32FC1 and has for each calculated
	/// profile one row with the following five entries:
	/// * 0 = x coordinate of the underlying edge in the image
	/// * 1 = y coordinate of the underlying edge in the image
	/// * 2 = width of the transition area (sharpness)
	/// * 3 = signal strength in the black cell (min brightness)
	/// * 4 = signal strength in the white cell (max brightness)
	///
	/// ## Returns
	/// Scalar(average sharpness, average min brightness, average max brightness,0)
	///
	/// ## Note
	/// This alternative version of [estimate_chessboard_sharpness] function uses the following default values for its arguments:
	/// * rise_distance: 0.8F
	/// * vertical: false
	/// * sharpness: noArray()
	#[inline]
	pub fn estimate_chessboard_sharpness_def(image: &impl ToInputArray, pattern_size: core::Size, corners: &impl ToInputArray) -> Result<core::Scalar> {
		input_array_arg!(image);
		input_array_arg!(corners);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_estimateChessboardSharpness_const__InputArrayR_Size_const__InputArrayR(image.as_raw__InputArray(), &pattern_size, corners.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Estimates the sharpness of a detected chessboard.
	///
	/// Image sharpness, as well as brightness, are a critical parameter for accuracte
	/// camera calibration. For accessing these parameters for filtering out
	/// problematic calibraiton images, this method calculates edge profiles by traveling from
	/// black to white chessboard cell centers. Based on this, the number of pixels is
	/// calculated required to transit from black to white. This width of the
	/// transition area is a good indication of how sharp the chessboard is imaged
	/// and should be below ~3.0 pixels.
	///
	/// ## Parameters
	/// * image: Gray image used to find chessboard corners
	/// * patternSize: Size of a found chessboard pattern
	/// * corners: Corners found by [find_chessboard_corners_sb]
	/// * rise_distance: Rise distance 0.8 means 10% ... 90% of the final signal strength
	/// * vertical: By default edge responses for horizontal lines are calculated
	/// * sharpness: Optional output array with a sharpness value for calculated edge responses (see description)
	///
	/// The optional sharpness array is of type CV_32FC1 and has for each calculated
	/// profile one row with the following five entries:
	/// * 0 = x coordinate of the underlying edge in the image
	/// * 1 = y coordinate of the underlying edge in the image
	/// * 2 = width of the transition area (sharpness)
	/// * 3 = signal strength in the black cell (min brightness)
	/// * 4 = signal strength in the white cell (max brightness)
	///
	/// ## Returns
	/// Scalar(average sharpness, average min brightness, average max brightness,0)
	///
	/// ## C++ default parameters
	/// * rise_distance: 0.8F
	/// * vertical: false
	/// * sharpness: noArray()
	#[inline]
	pub fn estimate_chessboard_sharpness(image: &impl ToInputArray, pattern_size: core::Size, corners: &impl ToInputArray, rise_distance: f32, vertical: bool, sharpness: &mut impl ToOutputArray) -> Result<core::Scalar> {
		input_array_arg!(image);
		input_array_arg!(corners);
		output_array_arg!(sharpness);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_estimateChessboardSharpness_const__InputArrayR_Size_const__InputArrayR_float_bool_const__OutputArrayR(image.as_raw__InputArray(), &pattern_size, corners.as_raw__InputArray(), rise_distance, vertical, sharpness.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes an optimal translation between two 3D point sets.
	///
	/// It computes
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Bbmatrix%7D%0Ax%5C%5C%0Ay%5C%5C%0Az%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%3D%0A%5Cbegin%7Bbmatrix%7D%0AX%5C%5C%0AY%5C%5C%0AZ%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%2B%0A%5Cbegin%7Bbmatrix%7D%0Ab%5F1%5C%5C%0Ab%5F2%5C%5C%0Ab%5F3%5C%5C%0A%5Cend%7Bbmatrix%7D%0A)
	///
	/// ## Parameters
	/// * src: First input 3D point set containing ![inline formula](https://latex.codecogs.com/png.latex?%28X%2CY%2CZ%29).
	/// * dst: Second input 3D point set containing ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%2Cz%29).
	/// * out: Output 3D translation vector ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%201) of the form
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Bbmatrix%7D%0Ab%5F1%20%5C%5C%0Ab%5F2%20%5C%5C%0Ab%5F3%20%5C%5C%0A%5Cend%7Bbmatrix%7D%0A)
	/// * inliers: Output vector indicating which points are inliers (1-inlier, 0-outlier).
	/// * ransacThreshold: Maximum reprojection error in the RANSAC algorithm to consider a point as
	/// an inlier.
	/// * confidence: Confidence level, between 0 and 1, for the estimated transformation. Anything
	/// between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
	/// significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
	///
	/// The function estimates an optimal 3D translation between two 3D point sets using the
	/// RANSAC algorithm.
	///
	/// ## Note
	/// This alternative version of [estimate_translation_3d] function uses the following default values for its arguments:
	/// * ransac_threshold: 3
	/// * confidence: 0.99
	#[inline]
	pub fn estimate_translation_3d_def(src: &impl ToInputArray, dst: &impl ToInputArray, out: &mut impl ToOutputArray, inliers: &mut impl ToOutputArray) -> Result<i32> {
		input_array_arg!(src);
		input_array_arg!(dst);
		output_array_arg!(out);
		output_array_arg!(inliers);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_estimateTranslation3D_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__InputArray(), out.as_raw__OutputArray(), inliers.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes an optimal translation between two 3D point sets.
	///
	/// It computes
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Bbmatrix%7D%0Ax%5C%5C%0Ay%5C%5C%0Az%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%3D%0A%5Cbegin%7Bbmatrix%7D%0AX%5C%5C%0AY%5C%5C%0AZ%5C%5C%0A%5Cend%7Bbmatrix%7D%0A%2B%0A%5Cbegin%7Bbmatrix%7D%0Ab%5F1%5C%5C%0Ab%5F2%5C%5C%0Ab%5F3%5C%5C%0A%5Cend%7Bbmatrix%7D%0A)
	///
	/// ## Parameters
	/// * src: First input 3D point set containing ![inline formula](https://latex.codecogs.com/png.latex?%28X%2CY%2CZ%29).
	/// * dst: Second input 3D point set containing ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%2Cz%29).
	/// * out: Output 3D translation vector ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%201) of the form
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Bbmatrix%7D%0Ab%5F1%20%5C%5C%0Ab%5F2%20%5C%5C%0Ab%5F3%20%5C%5C%0A%5Cend%7Bbmatrix%7D%0A)
	/// * inliers: Output vector indicating which points are inliers (1-inlier, 0-outlier).
	/// * ransacThreshold: Maximum reprojection error in the RANSAC algorithm to consider a point as
	/// an inlier.
	/// * confidence: Confidence level, between 0 and 1, for the estimated transformation. Anything
	/// between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
	/// significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
	///
	/// The function estimates an optimal 3D translation between two 3D point sets using the
	/// RANSAC algorithm.
	///
	/// ## C++ default parameters
	/// * ransac_threshold: 3
	/// * confidence: 0.99
	#[inline]
	pub fn estimate_translation_3d(src: &impl ToInputArray, dst: &impl ToInputArray, out: &mut impl ToOutputArray, inliers: &mut impl ToOutputArray, ransac_threshold: f64, confidence: f64) -> Result<i32> {
		input_array_arg!(src);
		input_array_arg!(dst);
		output_array_arg!(out);
		output_array_arg!(inliers);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_estimateTranslation3D_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_double_double(src.as_raw__InputArray(), dst.as_raw__InputArray(), out.as_raw__OutputArray(), inliers.as_raw__OutputArray(), ransac_threshold, confidence, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Filters homography decompositions based on additional information.
	///
	/// ## Parameters
	/// * rotations: Vector of rotation matrices.
	/// * normals: Vector of plane normal matrices.
	/// * beforePoints: Vector of (rectified) visible reference points before the homography is applied
	/// * afterPoints: Vector of (rectified) visible reference points after the homography is applied
	/// * possibleSolutions: Vector of int indices representing the viable solution set after filtering
	/// * pointsMask: optional Mat/Vector of 8u type representing the mask for the inliers as given by the [find_homography] function
	///
	/// This function is intended to filter the output of the [decompose_homography_mat] based on additional
	/// information as described in [Malis2007](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Malis2007) . The summary of the method: the [decompose_homography_mat] function
	/// returns 2 unique solutions and their "opposites" for a total of 4 solutions. If we have access to the
	/// sets of points visible in the camera frame before and after the homography transformation is applied,
	/// we can determine which are the true potential solutions and which are the opposites by verifying which
	/// homographies are consistent with all visible reference points being in front of the camera. The inputs
	/// are left unchanged; the filtered solution set is returned as indices into the existing one.
	///
	/// ## Note
	/// This alternative version of [filter_homography_decomp_by_visible_refpoints] function uses the following default values for its arguments:
	/// * points_mask: noArray()
	#[inline]
	pub fn filter_homography_decomp_by_visible_refpoints_def(rotations: &impl ToInputArray, normals: &impl ToInputArray, before_points: &impl ToInputArray, after_points: &impl ToInputArray, possible_solutions: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(rotations);
		input_array_arg!(normals);
		input_array_arg!(before_points);
		input_array_arg!(after_points);
		output_array_arg!(possible_solutions);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_filterHomographyDecompByVisibleRefpoints_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(rotations.as_raw__InputArray(), normals.as_raw__InputArray(), before_points.as_raw__InputArray(), after_points.as_raw__InputArray(), possible_solutions.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Filters homography decompositions based on additional information.
	///
	/// ## Parameters
	/// * rotations: Vector of rotation matrices.
	/// * normals: Vector of plane normal matrices.
	/// * beforePoints: Vector of (rectified) visible reference points before the homography is applied
	/// * afterPoints: Vector of (rectified) visible reference points after the homography is applied
	/// * possibleSolutions: Vector of int indices representing the viable solution set after filtering
	/// * pointsMask: optional Mat/Vector of 8u type representing the mask for the inliers as given by the [find_homography] function
	///
	/// This function is intended to filter the output of the [decompose_homography_mat] based on additional
	/// information as described in [Malis2007](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Malis2007) . The summary of the method: the [decompose_homography_mat] function
	/// returns 2 unique solutions and their "opposites" for a total of 4 solutions. If we have access to the
	/// sets of points visible in the camera frame before and after the homography transformation is applied,
	/// we can determine which are the true potential solutions and which are the opposites by verifying which
	/// homographies are consistent with all visible reference points being in front of the camera. The inputs
	/// are left unchanged; the filtered solution set is returned as indices into the existing one.
	///
	/// ## C++ default parameters
	/// * points_mask: noArray()
	#[inline]
	pub fn filter_homography_decomp_by_visible_refpoints(rotations: &impl ToInputArray, normals: &impl ToInputArray, before_points: &impl ToInputArray, after_points: &impl ToInputArray, possible_solutions: &mut impl ToOutputArray, points_mask: &impl ToInputArray) -> Result<()> {
		input_array_arg!(rotations);
		input_array_arg!(normals);
		input_array_arg!(before_points);
		input_array_arg!(after_points);
		output_array_arg!(possible_solutions);
		input_array_arg!(points_mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_filterHomographyDecompByVisibleRefpoints_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR(rotations.as_raw__InputArray(), normals.as_raw__InputArray(), before_points.as_raw__InputArray(), after_points.as_raw__InputArray(), possible_solutions.as_raw__OutputArray(), points_mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Filters off small noise blobs (speckles) in the disparity map
	///
	/// ## Parameters
	/// * img: The input 16-bit signed disparity image
	/// * newVal: The disparity value used to paint-off the speckles
	/// * maxSpeckleSize: The maximum speckle size to consider it a speckle. Larger blobs are not
	/// affected by the algorithm
	/// * maxDiff: Maximum difference between neighbor disparity pixels to put them into the same
	/// blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point
	/// disparity map, where disparity values are multiplied by 16, this scale factor should be taken into
	/// account when specifying this parameter value.
	/// * buf: The optional temporary buffer to avoid memory allocation within the function.
	///
	/// ## Note
	/// This alternative version of [filter_speckles] function uses the following default values for its arguments:
	/// * buf: noArray()
	#[inline]
	pub fn filter_speckles_def(img: &mut impl ToInputOutputArray, new_val: f64, max_speckle_size: i32, max_diff: f64) -> Result<()> {
		input_output_array_arg!(img);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_filterSpeckles_const__InputOutputArrayR_double_int_double(img.as_raw__InputOutputArray(), new_val, max_speckle_size, max_diff, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Filters off small noise blobs (speckles) in the disparity map
	///
	/// ## Parameters
	/// * img: The input 16-bit signed disparity image
	/// * newVal: The disparity value used to paint-off the speckles
	/// * maxSpeckleSize: The maximum speckle size to consider it a speckle. Larger blobs are not
	/// affected by the algorithm
	/// * maxDiff: Maximum difference between neighbor disparity pixels to put them into the same
	/// blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point
	/// disparity map, where disparity values are multiplied by 16, this scale factor should be taken into
	/// account when specifying this parameter value.
	/// * buf: The optional temporary buffer to avoid memory allocation within the function.
	///
	/// ## C++ default parameters
	/// * buf: noArray()
	#[inline]
	pub fn filter_speckles(img: &mut impl ToInputOutputArray, new_val: f64, max_speckle_size: i32, max_diff: f64, buf: &mut impl ToInputOutputArray) -> Result<()> {
		input_output_array_arg!(img);
		input_output_array_arg!(buf);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_filterSpeckles_const__InputOutputArrayR_double_int_double_const__InputOutputArrayR(img.as_raw__InputOutputArray(), new_val, max_speckle_size, max_diff, buf.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// finds subpixel-accurate positions of the chessboard corners
	#[inline]
	pub fn find4_quad_corner_subpix(img: &impl ToInputArray, corners: &mut impl ToInputOutputArray, region_size: core::Size) -> Result<bool> {
		input_array_arg!(img);
		input_output_array_arg!(corners);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_find4QuadCornerSubpix_const__InputArrayR_const__InputOutputArrayR_Size(img.as_raw__InputArray(), corners.as_raw__InputOutputArray(), &region_size, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds the positions of internal corners of the chessboard using a sector based approach.
	///
	/// ## Parameters
	/// * image: Source chessboard view. It must be an 8-bit grayscale or color image.
	/// * patternSize: Number of inner corners per a chessboard row and column
	/// ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
	/// * corners: Output array of detected corners.
	/// * flags: Various operation flags that can be zero or a combination of the following values:
	/// *   [CALIB_CB_NORMALIZE_IMAGE] Normalize the image gamma with equalizeHist before detection.
	/// *   [CALIB_CB_EXHAUSTIVE] Run an exhaustive search to improve detection rate.
	/// *   [CALIB_CB_ACCURACY] Up sample input image to improve sub-pixel accuracy due to aliasing effects.
	/// *   [CALIB_CB_LARGER] The detected pattern is allowed to be larger than patternSize (see description).
	/// *   [CALIB_CB_MARKER] The detected pattern must have a marker (see description).
	/// This should be used if an accurate camera calibration is required.
	/// * meta: Optional output arrray of detected corners (CV_8UC1 and size = cv::Size(columns,rows)).
	/// Each entry stands for one corner of the pattern and can have one of the following values:
	/// *   0 = no meta data attached
	/// *   1 = left-top corner of a black cell
	/// *   2 = left-top corner of a white cell
	/// *   3 = left-top corner of a black cell with a white marker dot
	/// *   4 = left-top corner of a white cell with a black marker dot (pattern origin in case of markers otherwise first corner)
	///
	/// The function is analog to [find_chessboard_corners] but uses a localized radon
	/// transformation approximated by box filters being more robust to all sort of
	/// noise, faster on larger images and is able to directly return the sub-pixel
	/// position of the internal chessboard corners. The Method is based on the paper
	/// [duda2018](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_duda2018) "Accurate Detection and Localization of Checkerboard Corners for
	/// Calibration" demonstrating that the returned sub-pixel positions are more
	/// accurate than the one returned by cornerSubPix allowing a precise camera
	/// calibration for demanding applications.
	///
	/// In the case, the flags [CALIB_CB_LARGER] or [CALIB_CB_MARKER] are given,
	/// the result can be recovered from the optional meta array. Both flags are
	/// helpful to use calibration patterns exceeding the field of view of the camera.
	/// These oversized patterns allow more accurate calibrations as corners can be
	/// utilized, which are as close as possible to the image borders.  For a
	/// consistent coordinate system across all images, the optional marker (see image
	/// below) can be used to move the origin of the board to the location where the
	/// black circle is located.
	///
	///
	/// Note: The function requires a white boarder with roughly the same width as one
	/// of the checkerboard fields around the whole board to improve the detection in
	/// various environments. In addition, because of the localized radon
	/// transformation it is beneficial to use round corners for the field corners
	/// which are located on the outside of the board. The following figure illustrates
	/// a sample checkerboard optimized for the detection. However, any other checkerboard
	/// can be used as well.
	///
	/// Use the `gen_pattern.py` Python script ([tutorial_camera_calibration_pattern])
	/// to create the corresponding checkerboard pattern:
	/// \image html pics/checkerboard_radon.png width=60%
	///
	/// ## Overloaded parameters
	///
	///
	/// ## Note
	/// This alternative version of [find_chessboard_corners_sb] function uses the following default values for its arguments:
	/// * flags: 0
	#[inline]
	pub fn find_chessboard_corners_sb_def(image: &impl ToInputArray, pattern_size: core::Size, corners: &mut impl ToOutputArray) -> Result<bool> {
		input_array_arg!(image);
		output_array_arg!(corners);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findChessboardCornersSB_const__InputArrayR_Size_const__OutputArrayR(image.as_raw__InputArray(), &pattern_size, corners.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds the positions of internal corners of the chessboard using a sector based approach.
	///
	/// ## Parameters
	/// * image: Source chessboard view. It must be an 8-bit grayscale or color image.
	/// * patternSize: Number of inner corners per a chessboard row and column
	/// ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
	/// * corners: Output array of detected corners.
	/// * flags: Various operation flags that can be zero or a combination of the following values:
	/// *   [CALIB_CB_NORMALIZE_IMAGE] Normalize the image gamma with equalizeHist before detection.
	/// *   [CALIB_CB_EXHAUSTIVE] Run an exhaustive search to improve detection rate.
	/// *   [CALIB_CB_ACCURACY] Up sample input image to improve sub-pixel accuracy due to aliasing effects.
	/// *   [CALIB_CB_LARGER] The detected pattern is allowed to be larger than patternSize (see description).
	/// *   [CALIB_CB_MARKER] The detected pattern must have a marker (see description).
	/// This should be used if an accurate camera calibration is required.
	/// * meta: Optional output arrray of detected corners (CV_8UC1 and size = cv::Size(columns,rows)).
	/// Each entry stands for one corner of the pattern and can have one of the following values:
	/// *   0 = no meta data attached
	/// *   1 = left-top corner of a black cell
	/// *   2 = left-top corner of a white cell
	/// *   3 = left-top corner of a black cell with a white marker dot
	/// *   4 = left-top corner of a white cell with a black marker dot (pattern origin in case of markers otherwise first corner)
	///
	/// The function is analog to [find_chessboard_corners] but uses a localized radon
	/// transformation approximated by box filters being more robust to all sort of
	/// noise, faster on larger images and is able to directly return the sub-pixel
	/// position of the internal chessboard corners. The Method is based on the paper
	/// [duda2018](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_duda2018) "Accurate Detection and Localization of Checkerboard Corners for
	/// Calibration" demonstrating that the returned sub-pixel positions are more
	/// accurate than the one returned by cornerSubPix allowing a precise camera
	/// calibration for demanding applications.
	///
	/// In the case, the flags [CALIB_CB_LARGER] or [CALIB_CB_MARKER] are given,
	/// the result can be recovered from the optional meta array. Both flags are
	/// helpful to use calibration patterns exceeding the field of view of the camera.
	/// These oversized patterns allow more accurate calibrations as corners can be
	/// utilized, which are as close as possible to the image borders.  For a
	/// consistent coordinate system across all images, the optional marker (see image
	/// below) can be used to move the origin of the board to the location where the
	/// black circle is located.
	///
	///
	/// Note: The function requires a white boarder with roughly the same width as one
	/// of the checkerboard fields around the whole board to improve the detection in
	/// various environments. In addition, because of the localized radon
	/// transformation it is beneficial to use round corners for the field corners
	/// which are located on the outside of the board. The following figure illustrates
	/// a sample checkerboard optimized for the detection. However, any other checkerboard
	/// can be used as well.
	///
	/// Use the `gen_pattern.py` Python script ([tutorial_camera_calibration_pattern])
	/// to create the corresponding checkerboard pattern:
	/// \image html pics/checkerboard_radon.png width=60%
	///
	/// ## Overloaded parameters
	///
	/// ## C++ default parameters
	/// * flags: 0
	#[inline]
	pub fn find_chessboard_corners_sb(image: &impl ToInputArray, pattern_size: core::Size, corners: &mut impl ToOutputArray, flags: i32) -> Result<bool> {
		input_array_arg!(image);
		output_array_arg!(corners);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findChessboardCornersSB_const__InputArrayR_Size_const__OutputArrayR_int(image.as_raw__InputArray(), &pattern_size, corners.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds the positions of internal corners of the chessboard using a sector based approach.
	///
	/// ## Parameters
	/// * image: Source chessboard view. It must be an 8-bit grayscale or color image.
	/// * patternSize: Number of inner corners per a chessboard row and column
	/// ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
	/// * corners: Output array of detected corners.
	/// * flags: Various operation flags that can be zero or a combination of the following values:
	/// *   [CALIB_CB_NORMALIZE_IMAGE] Normalize the image gamma with equalizeHist before detection.
	/// *   [CALIB_CB_EXHAUSTIVE] Run an exhaustive search to improve detection rate.
	/// *   [CALIB_CB_ACCURACY] Up sample input image to improve sub-pixel accuracy due to aliasing effects.
	/// *   [CALIB_CB_LARGER] The detected pattern is allowed to be larger than patternSize (see description).
	/// *   [CALIB_CB_MARKER] The detected pattern must have a marker (see description).
	/// This should be used if an accurate camera calibration is required.
	/// * meta: Optional output arrray of detected corners (CV_8UC1 and size = cv::Size(columns,rows)).
	/// Each entry stands for one corner of the pattern and can have one of the following values:
	/// *   0 = no meta data attached
	/// *   1 = left-top corner of a black cell
	/// *   2 = left-top corner of a white cell
	/// *   3 = left-top corner of a black cell with a white marker dot
	/// *   4 = left-top corner of a white cell with a black marker dot (pattern origin in case of markers otherwise first corner)
	///
	/// The function is analog to [find_chessboard_corners] but uses a localized radon
	/// transformation approximated by box filters being more robust to all sort of
	/// noise, faster on larger images and is able to directly return the sub-pixel
	/// position of the internal chessboard corners. The Method is based on the paper
	/// [duda2018](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_duda2018) "Accurate Detection and Localization of Checkerboard Corners for
	/// Calibration" demonstrating that the returned sub-pixel positions are more
	/// accurate than the one returned by cornerSubPix allowing a precise camera
	/// calibration for demanding applications.
	///
	/// In the case, the flags [CALIB_CB_LARGER] or [CALIB_CB_MARKER] are given,
	/// the result can be recovered from the optional meta array. Both flags are
	/// helpful to use calibration patterns exceeding the field of view of the camera.
	/// These oversized patterns allow more accurate calibrations as corners can be
	/// utilized, which are as close as possible to the image borders.  For a
	/// consistent coordinate system across all images, the optional marker (see image
	/// below) can be used to move the origin of the board to the location where the
	/// black circle is located.
	///
	///
	/// Note: The function requires a white boarder with roughly the same width as one
	/// of the checkerboard fields around the whole board to improve the detection in
	/// various environments. In addition, because of the localized radon
	/// transformation it is beneficial to use round corners for the field corners
	/// which are located on the outside of the board. The following figure illustrates
	/// a sample checkerboard optimized for the detection. However, any other checkerboard
	/// can be used as well.
	///
	/// Use the `gen_pattern.py` Python script ([tutorial_camera_calibration_pattern])
	/// to create the corresponding checkerboard pattern:
	/// \image html pics/checkerboard_radon.png width=60%
	#[inline]
	pub fn find_chessboard_corners_sb_with_meta(image: &impl ToInputArray, pattern_size: core::Size, corners: &mut impl ToOutputArray, flags: i32, meta: &mut impl ToOutputArray) -> Result<bool> {
		input_array_arg!(image);
		output_array_arg!(corners);
		output_array_arg!(meta);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findChessboardCornersSB_const__InputArrayR_Size_const__OutputArrayR_int_const__OutputArrayR(image.as_raw__InputArray(), &pattern_size, corners.as_raw__OutputArray(), flags, meta.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds the positions of internal corners of the chessboard.
	///
	/// ## Parameters
	/// * image: Source chessboard view. It must be an 8-bit grayscale or color image.
	/// * patternSize: Number of inner corners per a chessboard row and column
	/// ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
	/// * corners: Output array of detected corners.
	/// * flags: Various operation flags that can be zero or a combination of the following values:
	/// *   [CALIB_CB_ADAPTIVE_THRESH] Use adaptive thresholding to convert the image to black
	/// and white, rather than a fixed threshold level (computed from the average image brightness).
	/// *   [CALIB_CB_NORMALIZE_IMAGE] Normalize the image gamma with [equalize_hist] before
	/// applying fixed or adaptive thresholding.
	/// *   [CALIB_CB_FILTER_QUADS] Use additional criteria (like contour area, perimeter,
	/// square-like shape) to filter out false quads extracted at the contour retrieval stage.
	/// *   [CALIB_CB_FAST_CHECK] Run a fast check on the image that looks for chessboard corners,
	/// and shortcut the call if none is found. This can drastically speed up the call in the
	/// degenerate condition when no chessboard is observed.
	/// *   [CALIB_CB_PLAIN] All other flags are ignored. The input image is taken as is.
	/// No image processing is done to improve to find the checkerboard. This has the effect of speeding up the
	/// execution of the function but could lead to not recognizing the checkerboard if the image
	/// is not previously binarized in the appropriate manner.
	///
	/// The function attempts to determine whether the input image is a view of the chessboard pattern and
	/// locate the internal chessboard corners. The function returns a non-zero value if all of the corners
	/// are found and they are placed in a certain order (row by row, left to right in every row).
	/// Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example,
	/// a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black
	/// squares touch each other. The detected coordinates are approximate, and to determine their positions
	/// more accurately, the function calls #cornerSubPix. You also may use the function [corner_sub_pix] with
	/// different parameters if returned coordinates are not accurate enough.
	///
	/// Sample usage of detecting and drawing chessboard corners: :
	/// ```C++
	///    Size patternsize(8,6); //interior number of corners
	///    Mat gray = ....; //source image
	///    vector<Point2f> corners; //this will be filled by the detected corners
	///
	///    //CALIB_CB_FAST_CHECK saves a lot of time on images
	///    //that do not contain any chessboard corners
	///    bool patternfound = findChessboardCorners(gray, patternsize, corners,
	///            CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
	///            + CALIB_CB_FAST_CHECK);
	///
	///    if(patternfound)
	///       cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
	///        TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
	///
	///    drawChessboardCorners(img, patternsize, Mat(corners), patternfound);
	/// ```
	///
	///
	/// Note: The function requires white space (like a square-thick border, the wider the better) around
	/// the board to make the detection more robust in various environments. Otherwise, if there is no
	/// border and the background is dark, the outer black squares cannot be segmented properly and so the
	/// square grouping and ordering algorithm fails.
	///
	/// Use the `gen_pattern.py` Python script ([tutorial_camera_calibration_pattern])
	/// to create the desired checkerboard pattern.
	///
	/// ## Note
	/// This alternative version of [find_chessboard_corners] function uses the following default values for its arguments:
	/// * flags: CALIB_CB_ADAPTIVE_THRESH+CALIB_CB_NORMALIZE_IMAGE
	#[inline]
	pub fn find_chessboard_corners_def(image: &impl ToInputArray, pattern_size: core::Size, corners: &mut impl ToOutputArray) -> Result<bool> {
		input_array_arg!(image);
		output_array_arg!(corners);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findChessboardCorners_const__InputArrayR_Size_const__OutputArrayR(image.as_raw__InputArray(), &pattern_size, corners.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds the positions of internal corners of the chessboard.
	///
	/// ## Parameters
	/// * image: Source chessboard view. It must be an 8-bit grayscale or color image.
	/// * patternSize: Number of inner corners per a chessboard row and column
	/// ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
	/// * corners: Output array of detected corners.
	/// * flags: Various operation flags that can be zero or a combination of the following values:
	/// *   [CALIB_CB_ADAPTIVE_THRESH] Use adaptive thresholding to convert the image to black
	/// and white, rather than a fixed threshold level (computed from the average image brightness).
	/// *   [CALIB_CB_NORMALIZE_IMAGE] Normalize the image gamma with [equalize_hist] before
	/// applying fixed or adaptive thresholding.
	/// *   [CALIB_CB_FILTER_QUADS] Use additional criteria (like contour area, perimeter,
	/// square-like shape) to filter out false quads extracted at the contour retrieval stage.
	/// *   [CALIB_CB_FAST_CHECK] Run a fast check on the image that looks for chessboard corners,
	/// and shortcut the call if none is found. This can drastically speed up the call in the
	/// degenerate condition when no chessboard is observed.
	/// *   [CALIB_CB_PLAIN] All other flags are ignored. The input image is taken as is.
	/// No image processing is done to improve to find the checkerboard. This has the effect of speeding up the
	/// execution of the function but could lead to not recognizing the checkerboard if the image
	/// is not previously binarized in the appropriate manner.
	///
	/// The function attempts to determine whether the input image is a view of the chessboard pattern and
	/// locate the internal chessboard corners. The function returns a non-zero value if all of the corners
	/// are found and they are placed in a certain order (row by row, left to right in every row).
	/// Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example,
	/// a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black
	/// squares touch each other. The detected coordinates are approximate, and to determine their positions
	/// more accurately, the function calls #cornerSubPix. You also may use the function [corner_sub_pix] with
	/// different parameters if returned coordinates are not accurate enough.
	///
	/// Sample usage of detecting and drawing chessboard corners: :
	/// ```C++
	///    Size patternsize(8,6); //interior number of corners
	///    Mat gray = ....; //source image
	///    vector<Point2f> corners; //this will be filled by the detected corners
	///
	///    //CALIB_CB_FAST_CHECK saves a lot of time on images
	///    //that do not contain any chessboard corners
	///    bool patternfound = findChessboardCorners(gray, patternsize, corners,
	///            CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
	///            + CALIB_CB_FAST_CHECK);
	///
	///    if(patternfound)
	///       cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
	///        TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
	///
	///    drawChessboardCorners(img, patternsize, Mat(corners), patternfound);
	/// ```
	///
	///
	/// Note: The function requires white space (like a square-thick border, the wider the better) around
	/// the board to make the detection more robust in various environments. Otherwise, if there is no
	/// border and the background is dark, the outer black squares cannot be segmented properly and so the
	/// square grouping and ordering algorithm fails.
	///
	/// Use the `gen_pattern.py` Python script ([tutorial_camera_calibration_pattern])
	/// to create the desired checkerboard pattern.
	///
	/// ## C++ default parameters
	/// * flags: CALIB_CB_ADAPTIVE_THRESH+CALIB_CB_NORMALIZE_IMAGE
	#[inline]
	pub fn find_chessboard_corners(image: &impl ToInputArray, pattern_size: core::Size, corners: &mut impl ToOutputArray, flags: i32) -> Result<bool> {
		input_array_arg!(image);
		output_array_arg!(corners);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findChessboardCorners_const__InputArrayR_Size_const__OutputArrayR_int(image.as_raw__InputArray(), &pattern_size, corners.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds centers in the grid of circles.
	///
	/// ## Parameters
	/// * image: grid view of input circles; it must be an 8-bit grayscale or color image.
	/// * patternSize: number of circles per row and column
	/// ( patternSize = Size(points_per_row, points_per_colum) ).
	/// * centers: output array of detected centers.
	/// * flags: various operation flags that can be one of the following values:
	/// *   [CALIB_CB_SYMMETRIC_GRID] uses symmetric pattern of circles.
	/// *   [CALIB_CB_ASYMMETRIC_GRID] uses asymmetric pattern of circles.
	/// *   [CALIB_CB_CLUSTERING] uses a special algorithm for grid detection. It is more robust to
	/// perspective distortions but much more sensitive to background clutter.
	/// * blobDetector: feature detector that finds blobs like dark circles on light background.
	///                    If `blobDetector` is NULL then `image` represents Point2f array of candidates.
	/// * parameters: struct for finding circles in a grid pattern.
	///
	/// The function attempts to determine whether the input image contains a grid of circles. If it is, the
	/// function locates centers of the circles. The function returns a non-zero value if all of the centers
	/// have been found and they have been placed in a certain order (row by row, left to right in every
	/// row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0.
	///
	/// Sample usage of detecting and drawing the centers of circles: :
	/// ```C++
	///    Size patternsize(7,7); //number of centers
	///    Mat gray = ...; //source image
	///    vector<Point2f> centers; //this will be filled by the detected centers
	///
	///    bool patternfound = findCirclesGrid(gray, patternsize, centers);
	///
	///    drawChessboardCorners(img, patternsize, Mat(centers), patternfound);
	/// ```
	///
	///
	/// Note: The function requires white space (like a square-thick border, the wider the better) around
	/// the board to make the detection more robust in various environments.
	///
	/// ## Overloaded parameters
	///
	///
	/// ## Note
	/// This alternative version of [find_circles_grid_1] function uses the following default values for its arguments:
	/// * flags: CALIB_CB_SYMMETRIC_GRID
	/// * blob_detector: SimpleBlobDetector::create()
	#[inline]
	pub fn find_circles_grid_1_def(image: &impl ToInputArray, pattern_size: core::Size, centers: &mut impl ToOutputArray) -> Result<bool> {
		input_array_arg!(image);
		output_array_arg!(centers);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findCirclesGrid_const__InputArrayR_Size_const__OutputArrayR(image.as_raw__InputArray(), &pattern_size, centers.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds centers in the grid of circles.
	///
	/// ## Parameters
	/// * image: grid view of input circles; it must be an 8-bit grayscale or color image.
	/// * patternSize: number of circles per row and column
	/// ( patternSize = Size(points_per_row, points_per_colum) ).
	/// * centers: output array of detected centers.
	/// * flags: various operation flags that can be one of the following values:
	/// *   [CALIB_CB_SYMMETRIC_GRID] uses symmetric pattern of circles.
	/// *   [CALIB_CB_ASYMMETRIC_GRID] uses asymmetric pattern of circles.
	/// *   [CALIB_CB_CLUSTERING] uses a special algorithm for grid detection. It is more robust to
	/// perspective distortions but much more sensitive to background clutter.
	/// * blobDetector: feature detector that finds blobs like dark circles on light background.
	///                    If `blobDetector` is NULL then `image` represents Point2f array of candidates.
	/// * parameters: struct for finding circles in a grid pattern.
	///
	/// The function attempts to determine whether the input image contains a grid of circles. If it is, the
	/// function locates centers of the circles. The function returns a non-zero value if all of the centers
	/// have been found and they have been placed in a certain order (row by row, left to right in every
	/// row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0.
	///
	/// Sample usage of detecting and drawing the centers of circles: :
	/// ```C++
	///    Size patternsize(7,7); //number of centers
	///    Mat gray = ...; //source image
	///    vector<Point2f> centers; //this will be filled by the detected centers
	///
	///    bool patternfound = findCirclesGrid(gray, patternsize, centers);
	///
	///    drawChessboardCorners(img, patternsize, Mat(centers), patternfound);
	/// ```
	///
	///
	/// Note: The function requires white space (like a square-thick border, the wider the better) around
	/// the board to make the detection more robust in various environments.
	///
	/// ## Overloaded parameters
	///
	/// ## C++ default parameters
	/// * flags: CALIB_CB_SYMMETRIC_GRID
	/// * blob_detector: SimpleBlobDetector::create()
	#[inline]
	pub fn find_circles_grid_1(image: &impl ToInputArray, pattern_size: core::Size, centers: &mut impl ToOutputArray, flags: i32, blob_detector: Option<&core::Ptr<crate::features2d::Feature2D>>) -> Result<bool> {
		input_array_arg!(image);
		output_array_arg!(centers);
		smart_ptr_option_arg!(ref blob_detector);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findCirclesGrid_const__InputArrayR_Size_const__OutputArrayR_int_const_PtrLFeature2DGR(image.as_raw__InputArray(), &pattern_size, centers.as_raw__OutputArray(), flags, blob_detector.as_raw_PtrOfFeature2D(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds centers in the grid of circles.
	///
	/// ## Parameters
	/// * image: grid view of input circles; it must be an 8-bit grayscale or color image.
	/// * patternSize: number of circles per row and column
	/// ( patternSize = Size(points_per_row, points_per_colum) ).
	/// * centers: output array of detected centers.
	/// * flags: various operation flags that can be one of the following values:
	/// *   [CALIB_CB_SYMMETRIC_GRID] uses symmetric pattern of circles.
	/// *   [CALIB_CB_ASYMMETRIC_GRID] uses asymmetric pattern of circles.
	/// *   [CALIB_CB_CLUSTERING] uses a special algorithm for grid detection. It is more robust to
	/// perspective distortions but much more sensitive to background clutter.
	/// * blobDetector: feature detector that finds blobs like dark circles on light background.
	///                    If `blobDetector` is NULL then `image` represents Point2f array of candidates.
	/// * parameters: struct for finding circles in a grid pattern.
	///
	/// The function attempts to determine whether the input image contains a grid of circles. If it is, the
	/// function locates centers of the circles. The function returns a non-zero value if all of the centers
	/// have been found and they have been placed in a certain order (row by row, left to right in every
	/// row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0.
	///
	/// Sample usage of detecting and drawing the centers of circles: :
	/// ```C++
	///    Size patternsize(7,7); //number of centers
	///    Mat gray = ...; //source image
	///    vector<Point2f> centers; //this will be filled by the detected centers
	///
	///    bool patternfound = findCirclesGrid(gray, patternsize, centers);
	///
	///    drawChessboardCorners(img, patternsize, Mat(centers), patternfound);
	/// ```
	///
	///
	/// Note: The function requires white space (like a square-thick border, the wider the better) around
	/// the board to make the detection more robust in various environments.
	#[inline]
	pub fn find_circles_grid(image: &impl ToInputArray, pattern_size: core::Size, centers: &mut impl ToOutputArray, flags: i32, blob_detector: Option<&core::Ptr<crate::features2d::Feature2D>>, parameters: crate::calib3d::CirclesGridFinderParameters) -> Result<bool> {
		input_array_arg!(image);
		output_array_arg!(centers);
		smart_ptr_option_arg!(ref blob_detector);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findCirclesGrid_const__InputArrayR_Size_const__OutputArrayR_int_const_PtrLFeature2DGR_const_CirclesGridFinderParametersR(image.as_raw__InputArray(), &pattern_size, centers.as_raw__OutputArray(), flags, blob_detector.as_raw_PtrOfFeature2D(), &parameters, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Calculates an essential matrix from the corresponding points in two images.
	///
	/// ## Parameters
	/// * points1: Array of N (N \>= 5) 2D points from the first image. The point coordinates should
	/// be floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1.
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// Note that this function assumes that points1 and points2 are feature points from cameras with the
	/// same camera intrinsic matrix. If this assumption does not hold for your use case, use another
	/// function overload or [undistort_points] with `P = cv::NoArray()` for both cameras to transform image
	/// points to normalized image coordinates, which are valid for the identity camera intrinsic matrix.
	/// When passing these coordinates, pass the identity matrix for this parameter.
	/// * method: Method for computing an essential matrix.
	/// *   [RANSAC] for the RANSAC algorithm.
	/// *   [LMEDS] for the LMedS algorithm.
	/// * prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
	/// confidence (probability) that the estimated matrix is correct.
	/// * threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * mask: Output array of N elements, every element of which is set to 0 for outliers and to 1
	/// for the other points. The array is computed only in the RANSAC and LMedS methods.
	/// * maxIters: The maximum number of robust method iterations.
	///
	/// This function estimates essential matrix based on the five-point algorithm solver in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03) .
	/// [SteweniusCFS](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_SteweniusCFS) is also a related. The epipolar geometry is described by the following equation:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Bp%5F2%3B%201%5D%5ET%20K%5E%7B%2DT%7D%20E%20K%5E%7B%2D1%7D%20%5Bp%5F1%3B%201%5D%20%3D%200)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?E) is an essential matrix, ![inline formula](https://latex.codecogs.com/png.latex?p%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) are corresponding points in the first and the
	/// second images, respectively. The result of this function may be passed further to
	/// [decompose_essential_mat] or [recover_pose] to recover the relative pose between cameras.
	///
	/// ## Overloaded parameters
	///
	/// * points1: Array of N (N \>= 5) 2D points from the first image. The point coordinates should
	/// be floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * focal: focal length of the camera. Note that this function assumes that points1 and points2
	/// are feature points from cameras with same focal length and principal point.
	/// * pp: principal point of the camera.
	/// * method: Method for computing a fundamental matrix.
	/// *   [RANSAC] for the RANSAC algorithm.
	/// *   [LMEDS] for the LMedS algorithm.
	/// * threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
	/// confidence (probability) that the estimated matrix is correct.
	/// * mask: Output array of N elements, every element of which is set to 0 for outliers and to 1
	/// for the other points. The array is computed only in the RANSAC and LMedS methods.
	/// * maxIters: The maximum number of robust method iterations.
	///
	/// This function differs from the one above that it computes camera intrinsic matrix from focal length and
	/// principal point:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?A%20%3D%0A%5Cbegin%7Bbmatrix%7D%0Af%20%26%200%20%26%20x%5F%7Bpp%7D%20%20%5C%5C%0A0%20%26%20f%20%26%20y%5F%7Bpp%7D%20%20%5C%5C%0A0%20%26%200%20%26%201%0A%5Cend%7Bbmatrix%7D)
	///
	/// ## Note
	/// This alternative version of [find_essential_mat_1] function uses the following default values for its arguments:
	/// * focal: 1.0
	/// * pp: Point2d(0,0)
	/// * method: RANSAC
	/// * prob: 0.999
	/// * threshold: 1.0
	/// * max_iters: 1000
	/// * mask: noArray()
	#[inline]
	pub fn find_essential_mat_1_def(points1: &impl ToInputArray, points2: &impl ToInputArray) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findEssentialMat_const__InputArrayR_const__InputArrayR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Calculates an essential matrix from the corresponding points in two images.
	///
	/// ## Parameters
	/// * points1: Array of N (N \>= 5) 2D points from the first image. The point coordinates should
	/// be floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1.
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// Note that this function assumes that points1 and points2 are feature points from cameras with the
	/// same camera intrinsic matrix. If this assumption does not hold for your use case, use another
	/// function overload or [undistort_points] with `P = cv::NoArray()` for both cameras to transform image
	/// points to normalized image coordinates, which are valid for the identity camera intrinsic matrix.
	/// When passing these coordinates, pass the identity matrix for this parameter.
	/// * method: Method for computing an essential matrix.
	/// *   [RANSAC] for the RANSAC algorithm.
	/// *   [LMEDS] for the LMedS algorithm.
	/// * prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
	/// confidence (probability) that the estimated matrix is correct.
	/// * threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * mask: Output array of N elements, every element of which is set to 0 for outliers and to 1
	/// for the other points. The array is computed only in the RANSAC and LMedS methods.
	/// * maxIters: The maximum number of robust method iterations.
	///
	/// This function estimates essential matrix based on the five-point algorithm solver in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03) .
	/// [SteweniusCFS](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_SteweniusCFS) is also a related. The epipolar geometry is described by the following equation:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Bp%5F2%3B%201%5D%5ET%20K%5E%7B%2DT%7D%20E%20K%5E%7B%2D1%7D%20%5Bp%5F1%3B%201%5D%20%3D%200)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?E) is an essential matrix, ![inline formula](https://latex.codecogs.com/png.latex?p%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) are corresponding points in the first and the
	/// second images, respectively. The result of this function may be passed further to
	/// [decompose_essential_mat] or [recover_pose] to recover the relative pose between cameras.
	///
	/// ## Note
	/// This alternative version of [find_essential_mat] function uses the following default values for its arguments:
	/// * method: RANSAC
	/// * prob: 0.999
	/// * threshold: 1.0
	/// * max_iters: 1000
	/// * mask: noArray()
	#[inline]
	pub fn find_essential_mat_def(points1: &impl ToInputArray, points2: &impl ToInputArray, camera_matrix: &impl ToInputArray) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		input_array_arg!(camera_matrix);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findEssentialMat_const__InputArrayR_const__InputArrayR_const__InputArrayR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
	///
	/// ## Parameters
	/// * points1: Array of N (N \>= 5) 2D points from the first image. The point coordinates should
	/// be floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1.
	/// * cameraMatrix1: Camera matrix for the first camera ![inline formula](https://latex.codecogs.com/png.latex?K%20%3D%20%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D) .
	/// * cameraMatrix2: Camera matrix for the second camera ![inline formula](https://latex.codecogs.com/png.latex?K%20%3D%20%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D) .
	/// * distCoeffs1: Input vector of distortion coefficients for the first camera
	/// ![inline formula](https://latex.codecogs.com/png.latex?%28k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%5B%2C%20k%5F3%5B%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%5B%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%20s%5F4%5B%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%5D%5D%5D%5D%29)
	/// of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
	/// * distCoeffs2: Input vector of distortion coefficients for the second camera
	/// ![inline formula](https://latex.codecogs.com/png.latex?%28k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%5B%2C%20k%5F3%5B%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%5B%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%20s%5F4%5B%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%5D%5D%5D%5D%29)
	/// of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
	/// * method: Method for computing an essential matrix.
	/// *   [RANSAC] for the RANSAC algorithm.
	/// *   [LMEDS] for the LMedS algorithm.
	/// * prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
	/// confidence (probability) that the estimated matrix is correct.
	/// * threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * mask: Output array of N elements, every element of which is set to 0 for outliers and to 1
	/// for the other points. The array is computed only in the RANSAC and LMedS methods.
	///
	/// This function estimates essential matrix based on the five-point algorithm solver in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03) .
	/// [SteweniusCFS](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_SteweniusCFS) is also a related. The epipolar geometry is described by the following equation:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Bp%5F2%3B%201%5D%5ET%20K%5E%7B%2DT%7D%20E%20K%5E%7B%2D1%7D%20%5Bp%5F1%3B%201%5D%20%3D%200)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?E) is an essential matrix, ![inline formula](https://latex.codecogs.com/png.latex?p%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) are corresponding points in the first and the
	/// second images, respectively. The result of this function may be passed further to
	/// [decompose_essential_mat] or  [recover_pose] to recover the relative pose between cameras.
	///
	/// ## Note
	/// This alternative version of [find_essential_mat_3] function uses the following default values for its arguments:
	/// * method: RANSAC
	/// * prob: 0.999
	/// * threshold: 1.0
	/// * mask: noArray()
	#[inline]
	pub fn find_essential_mat_3_def(points1: &impl ToInputArray, points2: &impl ToInputArray, camera_matrix1: &impl ToInputArray, dist_coeffs1: &impl ToInputArray, camera_matrix2: &impl ToInputArray, dist_coeffs2: &impl ToInputArray) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		input_array_arg!(camera_matrix1);
		input_array_arg!(dist_coeffs1);
		input_array_arg!(camera_matrix2);
		input_array_arg!(dist_coeffs2);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findEssentialMat_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), camera_matrix1.as_raw__InputArray(), dist_coeffs1.as_raw__InputArray(), camera_matrix2.as_raw__InputArray(), dist_coeffs2.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	#[inline]
	pub fn find_essential_mat_4(points1: &impl ToInputArray, points2: &impl ToInputArray, camera_matrix1: &impl ToInputArray, camera_matrix2: &impl ToInputArray, dist_coeff1: &impl ToInputArray, dist_coeff2: &impl ToInputArray, mask: &mut impl ToOutputArray, params: crate::calib3d::UsacParams) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		input_array_arg!(camera_matrix1);
		input_array_arg!(camera_matrix2);
		input_array_arg!(dist_coeff1);
		input_array_arg!(dist_coeff2);
		output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findEssentialMat_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const_UsacParamsR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), camera_matrix1.as_raw__InputArray(), camera_matrix2.as_raw__InputArray(), dist_coeff1.as_raw__InputArray(), dist_coeff2.as_raw__InputArray(), mask.as_raw__OutputArray(), &params, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
	///
	/// ## Parameters
	/// * points1: Array of N (N \>= 5) 2D points from the first image. The point coordinates should
	/// be floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1.
	/// * cameraMatrix1: Camera matrix for the first camera ![inline formula](https://latex.codecogs.com/png.latex?K%20%3D%20%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D) .
	/// * cameraMatrix2: Camera matrix for the second camera ![inline formula](https://latex.codecogs.com/png.latex?K%20%3D%20%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D) .
	/// * distCoeffs1: Input vector of distortion coefficients for the first camera
	/// ![inline formula](https://latex.codecogs.com/png.latex?%28k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%5B%2C%20k%5F3%5B%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%5B%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%20s%5F4%5B%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%5D%5D%5D%5D%29)
	/// of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
	/// * distCoeffs2: Input vector of distortion coefficients for the second camera
	/// ![inline formula](https://latex.codecogs.com/png.latex?%28k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%5B%2C%20k%5F3%5B%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%5B%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%20s%5F4%5B%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%5D%5D%5D%5D%29)
	/// of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
	/// * method: Method for computing an essential matrix.
	/// *   [RANSAC] for the RANSAC algorithm.
	/// *   [LMEDS] for the LMedS algorithm.
	/// * prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
	/// confidence (probability) that the estimated matrix is correct.
	/// * threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * mask: Output array of N elements, every element of which is set to 0 for outliers and to 1
	/// for the other points. The array is computed only in the RANSAC and LMedS methods.
	///
	/// This function estimates essential matrix based on the five-point algorithm solver in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03) .
	/// [SteweniusCFS](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_SteweniusCFS) is also a related. The epipolar geometry is described by the following equation:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Bp%5F2%3B%201%5D%5ET%20K%5E%7B%2DT%7D%20E%20K%5E%7B%2D1%7D%20%5Bp%5F1%3B%201%5D%20%3D%200)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?E) is an essential matrix, ![inline formula](https://latex.codecogs.com/png.latex?p%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) are corresponding points in the first and the
	/// second images, respectively. The result of this function may be passed further to
	/// [decompose_essential_mat] or  [recover_pose] to recover the relative pose between cameras.
	///
	/// ## C++ default parameters
	/// * method: RANSAC
	/// * prob: 0.999
	/// * threshold: 1.0
	/// * mask: noArray()
	#[inline]
	pub fn find_essential_mat_3(points1: &impl ToInputArray, points2: &impl ToInputArray, camera_matrix1: &impl ToInputArray, dist_coeffs1: &impl ToInputArray, camera_matrix2: &impl ToInputArray, dist_coeffs2: &impl ToInputArray, method: i32, prob: f64, threshold: f64, mask: &mut impl ToOutputArray) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		input_array_arg!(camera_matrix1);
		input_array_arg!(dist_coeffs1);
		input_array_arg!(camera_matrix2);
		input_array_arg!(dist_coeffs2);
		output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findEssentialMat_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_int_double_double_const__OutputArrayR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), camera_matrix1.as_raw__InputArray(), dist_coeffs1.as_raw__InputArray(), camera_matrix2.as_raw__InputArray(), dist_coeffs2.as_raw__InputArray(), method, prob, threshold, mask.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Calculates an essential matrix from the corresponding points in two images.
	///
	/// ## Parameters
	/// * points1: Array of N (N \>= 5) 2D points from the first image. The point coordinates should
	/// be floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1.
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// Note that this function assumes that points1 and points2 are feature points from cameras with the
	/// same camera intrinsic matrix. If this assumption does not hold for your use case, use another
	/// function overload or [undistort_points] with `P = cv::NoArray()` for both cameras to transform image
	/// points to normalized image coordinates, which are valid for the identity camera intrinsic matrix.
	/// When passing these coordinates, pass the identity matrix for this parameter.
	/// * method: Method for computing an essential matrix.
	/// *   [RANSAC] for the RANSAC algorithm.
	/// *   [LMEDS] for the LMedS algorithm.
	/// * prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
	/// confidence (probability) that the estimated matrix is correct.
	/// * threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * mask: Output array of N elements, every element of which is set to 0 for outliers and to 1
	/// for the other points. The array is computed only in the RANSAC and LMedS methods.
	/// * maxIters: The maximum number of robust method iterations.
	///
	/// This function estimates essential matrix based on the five-point algorithm solver in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03) .
	/// [SteweniusCFS](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_SteweniusCFS) is also a related. The epipolar geometry is described by the following equation:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Bp%5F2%3B%201%5D%5ET%20K%5E%7B%2DT%7D%20E%20K%5E%7B%2D1%7D%20%5Bp%5F1%3B%201%5D%20%3D%200)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?E) is an essential matrix, ![inline formula](https://latex.codecogs.com/png.latex?p%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) are corresponding points in the first and the
	/// second images, respectively. The result of this function may be passed further to
	/// [decompose_essential_mat] or [recover_pose] to recover the relative pose between cameras.
	///
	/// ## Overloaded parameters
	#[inline]
	pub fn find_essential_mat_matrix(points1: &impl ToInputArray, points2: &impl ToInputArray, camera_matrix: &impl ToInputArray, method: i32, prob: f64, threshold: f64, mask: &mut impl ToOutputArray) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		input_array_arg!(camera_matrix);
		output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findEssentialMat_const__InputArrayR_const__InputArrayR_const__InputArrayR_int_double_double_const__OutputArrayR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), method, prob, threshold, mask.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Calculates an essential matrix from the corresponding points in two images.
	///
	/// ## Parameters
	/// * points1: Array of N (N \>= 5) 2D points from the first image. The point coordinates should
	/// be floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1.
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// Note that this function assumes that points1 and points2 are feature points from cameras with the
	/// same camera intrinsic matrix. If this assumption does not hold for your use case, use another
	/// function overload or [undistort_points] with `P = cv::NoArray()` for both cameras to transform image
	/// points to normalized image coordinates, which are valid for the identity camera intrinsic matrix.
	/// When passing these coordinates, pass the identity matrix for this parameter.
	/// * method: Method for computing an essential matrix.
	/// *   [RANSAC] for the RANSAC algorithm.
	/// *   [LMEDS] for the LMedS algorithm.
	/// * prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
	/// confidence (probability) that the estimated matrix is correct.
	/// * threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * mask: Output array of N elements, every element of which is set to 0 for outliers and to 1
	/// for the other points. The array is computed only in the RANSAC and LMedS methods.
	/// * maxIters: The maximum number of robust method iterations.
	///
	/// This function estimates essential matrix based on the five-point algorithm solver in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03) .
	/// [SteweniusCFS](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_SteweniusCFS) is also a related. The epipolar geometry is described by the following equation:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Bp%5F2%3B%201%5D%5ET%20K%5E%7B%2DT%7D%20E%20K%5E%7B%2D1%7D%20%5Bp%5F1%3B%201%5D%20%3D%200)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?E) is an essential matrix, ![inline formula](https://latex.codecogs.com/png.latex?p%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) are corresponding points in the first and the
	/// second images, respectively. The result of this function may be passed further to
	/// [decompose_essential_mat] or [recover_pose] to recover the relative pose between cameras.
	///
	/// ## C++ default parameters
	/// * method: RANSAC
	/// * prob: 0.999
	/// * threshold: 1.0
	/// * max_iters: 1000
	/// * mask: noArray()
	#[inline]
	pub fn find_essential_mat(points1: &impl ToInputArray, points2: &impl ToInputArray, camera_matrix: &impl ToInputArray, method: i32, prob: f64, threshold: f64, max_iters: i32, mask: &mut impl ToOutputArray) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		input_array_arg!(camera_matrix);
		output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findEssentialMat_const__InputArrayR_const__InputArrayR_const__InputArrayR_int_double_double_int_const__OutputArrayR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), method, prob, threshold, max_iters, mask.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Calculates an essential matrix from the corresponding points in two images.
	///
	/// ## Parameters
	/// * points1: Array of N (N \>= 5) 2D points from the first image. The point coordinates should
	/// be floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1.
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// Note that this function assumes that points1 and points2 are feature points from cameras with the
	/// same camera intrinsic matrix. If this assumption does not hold for your use case, use another
	/// function overload or [undistort_points] with `P = cv::NoArray()` for both cameras to transform image
	/// points to normalized image coordinates, which are valid for the identity camera intrinsic matrix.
	/// When passing these coordinates, pass the identity matrix for this parameter.
	/// * method: Method for computing an essential matrix.
	/// *   [RANSAC] for the RANSAC algorithm.
	/// *   [LMEDS] for the LMedS algorithm.
	/// * prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
	/// confidence (probability) that the estimated matrix is correct.
	/// * threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * mask: Output array of N elements, every element of which is set to 0 for outliers and to 1
	/// for the other points. The array is computed only in the RANSAC and LMedS methods.
	/// * maxIters: The maximum number of robust method iterations.
	///
	/// This function estimates essential matrix based on the five-point algorithm solver in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03) .
	/// [SteweniusCFS](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_SteweniusCFS) is also a related. The epipolar geometry is described by the following equation:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Bp%5F2%3B%201%5D%5ET%20K%5E%7B%2DT%7D%20E%20K%5E%7B%2D1%7D%20%5Bp%5F1%3B%201%5D%20%3D%200)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?E) is an essential matrix, ![inline formula](https://latex.codecogs.com/png.latex?p%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) are corresponding points in the first and the
	/// second images, respectively. The result of this function may be passed further to
	/// [decompose_essential_mat] or [recover_pose] to recover the relative pose between cameras.
	///
	/// ## Overloaded parameters
	#[inline]
	pub fn find_essential_mat_2(points1: &impl ToInputArray, points2: &impl ToInputArray, focal: f64, pp: core::Point2d, method: i32, prob: f64, threshold: f64, mask: &mut impl ToOutputArray) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findEssentialMat_const__InputArrayR_const__InputArrayR_double_Point2d_int_double_double_const__OutputArrayR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), focal, &pp, method, prob, threshold, mask.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Calculates an essential matrix from the corresponding points in two images.
	///
	/// ## Parameters
	/// * points1: Array of N (N \>= 5) 2D points from the first image. The point coordinates should
	/// be floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1.
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// Note that this function assumes that points1 and points2 are feature points from cameras with the
	/// same camera intrinsic matrix. If this assumption does not hold for your use case, use another
	/// function overload or [undistort_points] with `P = cv::NoArray()` for both cameras to transform image
	/// points to normalized image coordinates, which are valid for the identity camera intrinsic matrix.
	/// When passing these coordinates, pass the identity matrix for this parameter.
	/// * method: Method for computing an essential matrix.
	/// *   [RANSAC] for the RANSAC algorithm.
	/// *   [LMEDS] for the LMedS algorithm.
	/// * prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
	/// confidence (probability) that the estimated matrix is correct.
	/// * threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * mask: Output array of N elements, every element of which is set to 0 for outliers and to 1
	/// for the other points. The array is computed only in the RANSAC and LMedS methods.
	/// * maxIters: The maximum number of robust method iterations.
	///
	/// This function estimates essential matrix based on the five-point algorithm solver in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03) .
	/// [SteweniusCFS](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_SteweniusCFS) is also a related. The epipolar geometry is described by the following equation:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Bp%5F2%3B%201%5D%5ET%20K%5E%7B%2DT%7D%20E%20K%5E%7B%2D1%7D%20%5Bp%5F1%3B%201%5D%20%3D%200)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?E) is an essential matrix, ![inline formula](https://latex.codecogs.com/png.latex?p%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) are corresponding points in the first and the
	/// second images, respectively. The result of this function may be passed further to
	/// [decompose_essential_mat] or [recover_pose] to recover the relative pose between cameras.
	///
	/// ## Overloaded parameters
	///
	/// * points1: Array of N (N \>= 5) 2D points from the first image. The point coordinates should
	/// be floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * focal: focal length of the camera. Note that this function assumes that points1 and points2
	/// are feature points from cameras with same focal length and principal point.
	/// * pp: principal point of the camera.
	/// * method: Method for computing a fundamental matrix.
	/// *   [RANSAC] for the RANSAC algorithm.
	/// *   [LMEDS] for the LMedS algorithm.
	/// * threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
	/// confidence (probability) that the estimated matrix is correct.
	/// * mask: Output array of N elements, every element of which is set to 0 for outliers and to 1
	/// for the other points. The array is computed only in the RANSAC and LMedS methods.
	/// * maxIters: The maximum number of robust method iterations.
	///
	/// This function differs from the one above that it computes camera intrinsic matrix from focal length and
	/// principal point:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?A%20%3D%0A%5Cbegin%7Bbmatrix%7D%0Af%20%26%200%20%26%20x%5F%7Bpp%7D%20%20%5C%5C%0A0%20%26%20f%20%26%20y%5F%7Bpp%7D%20%20%5C%5C%0A0%20%26%200%20%26%201%0A%5Cend%7Bbmatrix%7D)
	///
	/// ## C++ default parameters
	/// * focal: 1.0
	/// * pp: Point2d(0,0)
	/// * method: RANSAC
	/// * prob: 0.999
	/// * threshold: 1.0
	/// * max_iters: 1000
	/// * mask: noArray()
	#[inline]
	pub fn find_essential_mat_1(points1: &impl ToInputArray, points2: &impl ToInputArray, focal: f64, pp: core::Point2d, method: i32, prob: f64, threshold: f64, max_iters: i32, mask: &mut impl ToOutputArray) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findEssentialMat_const__InputArrayR_const__InputArrayR_double_Point2d_int_double_double_int_const__OutputArrayR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), focal, &pp, method, prob, threshold, max_iters, mask.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Calculates a fundamental matrix from the corresponding points in two images.
	///
	/// ## Parameters
	/// * points1: Array of N points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * method: Method for computing a fundamental matrix.
	/// *   [FM_7POINT] for a 7-point algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%3D%207)
	/// *   [FM_8POINT] for an 8-point algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// *   [FM_RANSAC] for the RANSAC algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// *   [FM_LMEDS] for the LMedS algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// * ransacReprojThreshold: Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * confidence: Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level
	/// of confidence (probability) that the estimated matrix is correct.
	/// * mask:[out] optional output mask
	/// * maxIters: The maximum number of robust method iterations.
	///
	/// The epipolar geometry is described by the following equation:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Bp%5F2%3B%201%5D%5ET%20F%20%5Bp%5F1%3B%201%5D%20%3D%200)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?F) is a fundamental matrix, ![inline formula](https://latex.codecogs.com/png.latex?p%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) are corresponding points in the first and the
	/// second images, respectively.
	///
	/// The function calculates the fundamental matrix using one of four methods listed above and returns
	/// the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point
	/// algorithm, the function may return up to 3 solutions ( ![inline formula](https://latex.codecogs.com/png.latex?9%20%5Ctimes%203) matrix that stores all 3
	/// matrices sequentially).
	///
	/// The calculated fundamental matrix may be passed further to [compute_correspond_epilines] that finds the
	/// epipolar lines corresponding to the specified points. It can also be passed to
	/// [stereo_rectify_uncalibrated] to compute the rectification transformation. :
	/// ```C++
	///    // Example. Estimation of fundamental matrix using the RANSAC algorithm
	///    int point_count = 100;
	///    vector<Point2f> points1(point_count);
	///    vector<Point2f> points2(point_count);
	///
	///    // initialize the points here ...
	///    for( int i = 0; i < point_count; i++ )
	///    {
	///        points1[i] = ...;
	///        points2[i] = ...;
	///    }
	///
	///    Mat fundamental_matrix =
	///      findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);
	/// ```
	///
	///
	/// ## Overloaded parameters
	///
	///
	/// ## Note
	/// This alternative version of [find_fundamental_mat_1] function uses the following default values for its arguments:
	/// * method: FM_RANSAC
	/// * ransac_reproj_threshold: 3.
	/// * confidence: 0.99
	/// * mask: noArray()
	#[inline]
	pub fn find_fundamental_mat_1_def(points1: &impl ToInputArray, points2: &impl ToInputArray) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findFundamentalMat_const__InputArrayR_const__InputArrayR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Calculates a fundamental matrix from the corresponding points in two images.
	///
	/// ## Parameters
	/// * points1: Array of N points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * method: Method for computing a fundamental matrix.
	/// *   [FM_7POINT] for a 7-point algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%3D%207)
	/// *   [FM_8POINT] for an 8-point algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// *   [FM_RANSAC] for the RANSAC algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// *   [FM_LMEDS] for the LMedS algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// * ransacReprojThreshold: Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * confidence: Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level
	/// of confidence (probability) that the estimated matrix is correct.
	/// * mask:[out] optional output mask
	/// * maxIters: The maximum number of robust method iterations.
	///
	/// The epipolar geometry is described by the following equation:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Bp%5F2%3B%201%5D%5ET%20F%20%5Bp%5F1%3B%201%5D%20%3D%200)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?F) is a fundamental matrix, ![inline formula](https://latex.codecogs.com/png.latex?p%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) are corresponding points in the first and the
	/// second images, respectively.
	///
	/// The function calculates the fundamental matrix using one of four methods listed above and returns
	/// the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point
	/// algorithm, the function may return up to 3 solutions ( ![inline formula](https://latex.codecogs.com/png.latex?9%20%5Ctimes%203) matrix that stores all 3
	/// matrices sequentially).
	///
	/// The calculated fundamental matrix may be passed further to [compute_correspond_epilines] that finds the
	/// epipolar lines corresponding to the specified points. It can also be passed to
	/// [stereo_rectify_uncalibrated] to compute the rectification transformation. :
	/// ```C++
	///    // Example. Estimation of fundamental matrix using the RANSAC algorithm
	///    int point_count = 100;
	///    vector<Point2f> points1(point_count);
	///    vector<Point2f> points2(point_count);
	///
	///    // initialize the points here ...
	///    for( int i = 0; i < point_count; i++ )
	///    {
	///        points1[i] = ...;
	///        points2[i] = ...;
	///    }
	///
	///    Mat fundamental_matrix =
	///      findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);
	/// ```
	///
	///
	/// ## Overloaded parameters
	///
	///
	/// ## Note
	/// This alternative version of [find_fundamental_mat_mask] function uses the following default values for its arguments:
	/// * method: FM_RANSAC
	/// * ransac_reproj_threshold: 3.
	/// * confidence: 0.99
	#[inline]
	pub fn find_fundamental_mat_mask_def(points1: &impl ToInputArray, points2: &impl ToInputArray, mask: &mut impl ToOutputArray) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findFundamentalMat_const__InputArrayR_const__InputArrayR_const__OutputArrayR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), mask.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	#[inline]
	pub fn find_fundamental_mat_2(points1: &impl ToInputArray, points2: &impl ToInputArray, mask: &mut impl ToOutputArray, params: crate::calib3d::UsacParams) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findFundamentalMat_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const_UsacParamsR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), mask.as_raw__OutputArray(), &params, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Calculates a fundamental matrix from the corresponding points in two images.
	///
	/// ## Parameters
	/// * points1: Array of N points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * method: Method for computing a fundamental matrix.
	/// *   [FM_7POINT] for a 7-point algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%3D%207)
	/// *   [FM_8POINT] for an 8-point algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// *   [FM_RANSAC] for the RANSAC algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// *   [FM_LMEDS] for the LMedS algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// * ransacReprojThreshold: Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * confidence: Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level
	/// of confidence (probability) that the estimated matrix is correct.
	/// * mask:[out] optional output mask
	/// * maxIters: The maximum number of robust method iterations.
	///
	/// The epipolar geometry is described by the following equation:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Bp%5F2%3B%201%5D%5ET%20F%20%5Bp%5F1%3B%201%5D%20%3D%200)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?F) is a fundamental matrix, ![inline formula](https://latex.codecogs.com/png.latex?p%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) are corresponding points in the first and the
	/// second images, respectively.
	///
	/// The function calculates the fundamental matrix using one of four methods listed above and returns
	/// the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point
	/// algorithm, the function may return up to 3 solutions ( ![inline formula](https://latex.codecogs.com/png.latex?9%20%5Ctimes%203) matrix that stores all 3
	/// matrices sequentially).
	///
	/// The calculated fundamental matrix may be passed further to [compute_correspond_epilines] that finds the
	/// epipolar lines corresponding to the specified points. It can also be passed to
	/// [stereo_rectify_uncalibrated] to compute the rectification transformation. :
	/// ```C++
	///    // Example. Estimation of fundamental matrix using the RANSAC algorithm
	///    int point_count = 100;
	///    vector<Point2f> points1(point_count);
	///    vector<Point2f> points2(point_count);
	///
	///    // initialize the points here ...
	///    for( int i = 0; i < point_count; i++ )
	///    {
	///        points1[i] = ...;
	///        points2[i] = ...;
	///    }
	///
	///    Mat fundamental_matrix =
	///      findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);
	/// ```
	///
	///
	/// ## Overloaded parameters
	///
	/// ## C++ default parameters
	/// * method: FM_RANSAC
	/// * ransac_reproj_threshold: 3.
	/// * confidence: 0.99
	#[inline]
	pub fn find_fundamental_mat_mask(points1: &impl ToInputArray, points2: &impl ToInputArray, mask: &mut impl ToOutputArray, method: i32, ransac_reproj_threshold: f64, confidence: f64) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findFundamentalMat_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_double_double(points1.as_raw__InputArray(), points2.as_raw__InputArray(), mask.as_raw__OutputArray(), method, ransac_reproj_threshold, confidence, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Calculates a fundamental matrix from the corresponding points in two images.
	///
	/// ## Parameters
	/// * points1: Array of N points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * method: Method for computing a fundamental matrix.
	/// *   [FM_7POINT] for a 7-point algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%3D%207)
	/// *   [FM_8POINT] for an 8-point algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// *   [FM_RANSAC] for the RANSAC algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// *   [FM_LMEDS] for the LMedS algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// * ransacReprojThreshold: Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * confidence: Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level
	/// of confidence (probability) that the estimated matrix is correct.
	/// * mask:[out] optional output mask
	/// * maxIters: The maximum number of robust method iterations.
	///
	/// The epipolar geometry is described by the following equation:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Bp%5F2%3B%201%5D%5ET%20F%20%5Bp%5F1%3B%201%5D%20%3D%200)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?F) is a fundamental matrix, ![inline formula](https://latex.codecogs.com/png.latex?p%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) are corresponding points in the first and the
	/// second images, respectively.
	///
	/// The function calculates the fundamental matrix using one of four methods listed above and returns
	/// the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point
	/// algorithm, the function may return up to 3 solutions ( ![inline formula](https://latex.codecogs.com/png.latex?9%20%5Ctimes%203) matrix that stores all 3
	/// matrices sequentially).
	///
	/// The calculated fundamental matrix may be passed further to [compute_correspond_epilines] that finds the
	/// epipolar lines corresponding to the specified points. It can also be passed to
	/// [stereo_rectify_uncalibrated] to compute the rectification transformation. :
	/// ```C++
	///    // Example. Estimation of fundamental matrix using the RANSAC algorithm
	///    int point_count = 100;
	///    vector<Point2f> points1(point_count);
	///    vector<Point2f> points2(point_count);
	///
	///    // initialize the points here ...
	///    for( int i = 0; i < point_count; i++ )
	///    {
	///        points1[i] = ...;
	///        points2[i] = ...;
	///    }
	///
	///    Mat fundamental_matrix =
	///      findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);
	/// ```
	///
	///
	/// ## Overloaded parameters
	///
	/// ## C++ default parameters
	/// * method: FM_RANSAC
	/// * ransac_reproj_threshold: 3.
	/// * confidence: 0.99
	/// * mask: noArray()
	#[inline]
	pub fn find_fundamental_mat_1(points1: &impl ToInputArray, points2: &impl ToInputArray, method: i32, ransac_reproj_threshold: f64, confidence: f64, mask: &mut impl ToOutputArray) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findFundamentalMat_const__InputArrayR_const__InputArrayR_int_double_double_const__OutputArrayR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), method, ransac_reproj_threshold, confidence, mask.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Calculates a fundamental matrix from the corresponding points in two images.
	///
	/// ## Parameters
	/// * points1: Array of N points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * method: Method for computing a fundamental matrix.
	/// *   [FM_7POINT] for a 7-point algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%3D%207)
	/// *   [FM_8POINT] for an 8-point algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// *   [FM_RANSAC] for the RANSAC algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// *   [FM_LMEDS] for the LMedS algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// * ransacReprojThreshold: Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * confidence: Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level
	/// of confidence (probability) that the estimated matrix is correct.
	/// * mask:[out] optional output mask
	/// * maxIters: The maximum number of robust method iterations.
	///
	/// The epipolar geometry is described by the following equation:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Bp%5F2%3B%201%5D%5ET%20F%20%5Bp%5F1%3B%201%5D%20%3D%200)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?F) is a fundamental matrix, ![inline formula](https://latex.codecogs.com/png.latex?p%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) are corresponding points in the first and the
	/// second images, respectively.
	///
	/// The function calculates the fundamental matrix using one of four methods listed above and returns
	/// the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point
	/// algorithm, the function may return up to 3 solutions ( ![inline formula](https://latex.codecogs.com/png.latex?9%20%5Ctimes%203) matrix that stores all 3
	/// matrices sequentially).
	///
	/// The calculated fundamental matrix may be passed further to [compute_correspond_epilines] that finds the
	/// epipolar lines corresponding to the specified points. It can also be passed to
	/// [stereo_rectify_uncalibrated] to compute the rectification transformation. :
	/// ```C++
	///    // Example. Estimation of fundamental matrix using the RANSAC algorithm
	///    int point_count = 100;
	///    vector<Point2f> points1(point_count);
	///    vector<Point2f> points2(point_count);
	///
	///    // initialize the points here ...
	///    for( int i = 0; i < point_count; i++ )
	///    {
	///        points1[i] = ...;
	///        points2[i] = ...;
	///    }
	///
	///    Mat fundamental_matrix =
	///      findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);
	/// ```
	///
	///
	/// ## Note
	/// This alternative version of [find_fundamental_mat] function uses the following default values for its arguments:
	/// * mask: noArray()
	#[inline]
	pub fn find_fundamental_mat_def(points1: &impl ToInputArray, points2: &impl ToInputArray, method: i32, ransac_reproj_threshold: f64, confidence: f64, max_iters: i32) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findFundamentalMat_const__InputArrayR_const__InputArrayR_int_double_double_int(points1.as_raw__InputArray(), points2.as_raw__InputArray(), method, ransac_reproj_threshold, confidence, max_iters, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Calculates a fundamental matrix from the corresponding points in two images.
	///
	/// ## Parameters
	/// * points1: Array of N points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * method: Method for computing a fundamental matrix.
	/// *   [FM_7POINT] for a 7-point algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%3D%207)
	/// *   [FM_8POINT] for an 8-point algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// *   [FM_RANSAC] for the RANSAC algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// *   [FM_LMEDS] for the LMedS algorithm. ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Cge%208)
	/// * ransacReprojThreshold: Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * confidence: Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level
	/// of confidence (probability) that the estimated matrix is correct.
	/// * mask:[out] optional output mask
	/// * maxIters: The maximum number of robust method iterations.
	///
	/// The epipolar geometry is described by the following equation:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Bp%5F2%3B%201%5D%5ET%20F%20%5Bp%5F1%3B%201%5D%20%3D%200)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?F) is a fundamental matrix, ![inline formula](https://latex.codecogs.com/png.latex?p%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?p%5F2) are corresponding points in the first and the
	/// second images, respectively.
	///
	/// The function calculates the fundamental matrix using one of four methods listed above and returns
	/// the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point
	/// algorithm, the function may return up to 3 solutions ( ![inline formula](https://latex.codecogs.com/png.latex?9%20%5Ctimes%203) matrix that stores all 3
	/// matrices sequentially).
	///
	/// The calculated fundamental matrix may be passed further to [compute_correspond_epilines] that finds the
	/// epipolar lines corresponding to the specified points. It can also be passed to
	/// [stereo_rectify_uncalibrated] to compute the rectification transformation. :
	/// ```C++
	///    // Example. Estimation of fundamental matrix using the RANSAC algorithm
	///    int point_count = 100;
	///    vector<Point2f> points1(point_count);
	///    vector<Point2f> points2(point_count);
	///
	///    // initialize the points here ...
	///    for( int i = 0; i < point_count; i++ )
	///    {
	///        points1[i] = ...;
	///        points2[i] = ...;
	///    }
	///
	///    Mat fundamental_matrix =
	///      findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);
	/// ```
	///
	///
	/// ## C++ default parameters
	/// * mask: noArray()
	#[inline]
	pub fn find_fundamental_mat(points1: &impl ToInputArray, points2: &impl ToInputArray, method: i32, ransac_reproj_threshold: f64, confidence: f64, max_iters: i32, mask: &mut impl ToOutputArray) -> Result<core::Mat> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findFundamentalMat_const__InputArrayR_const__InputArrayR_int_double_double_int_const__OutputArrayR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), method, ransac_reproj_threshold, confidence, max_iters, mask.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Finds a perspective transformation between two planes.
	///
	/// ## Parameters
	/// * srcPoints: Coordinates of the points in the original plane, a matrix of the type CV_32FC2
	/// or vector\<Point2f\> .
	/// * dstPoints: Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or
	/// a vector\<Point2f\> .
	/// * method: Method used to compute a homography matrix. The following methods are possible:
	/// *   **0** - a regular method using all the points, i.e., the least squares method
	/// *   [RANSAC] - RANSAC-based robust method
	/// *   [LMEDS] - Least-Median robust method
	/// *   [RHO] - PROSAC-based robust method
	/// * ransacReprojThreshold: Maximum allowed reprojection error to treat a point pair as an inlier
	/// (used in the RANSAC and RHO methods only). That is, if
	/// ![block formula](https://latex.codecogs.com/png.latex?%5C%7C%20%5Ctexttt%7BdstPoints%7D%20%5Fi%20%2D%20%20%5Ctexttt%7BconvertPointsHomogeneous%7D%20%28%20%5Ctexttt%7BH%7D%20%5Ccdot%20%5Ctexttt%7BsrcPoints%7D%20%5Fi%29%20%5C%7C%5F2%20%20%3E%20%20%5Ctexttt%7BransacReprojThreshold%7D)
	/// then the point ![inline formula](https://latex.codecogs.com/png.latex?i) is considered as an outlier. If srcPoints and dstPoints are measured in pixels,
	/// it usually makes sense to set this parameter somewhere in the range of 1 to 10.
	/// * mask: Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input
	/// mask values are ignored.
	/// * maxIters: The maximum number of RANSAC iterations.
	/// * confidence: Confidence level, between 0 and 1.
	///
	/// The function finds and returns the perspective transformation ![inline formula](https://latex.codecogs.com/png.latex?H) between the source and the
	/// destination planes:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?s%5Fi%20%20%5Cbegin%7Bbmatrix%7D%20x%27%5Fi%5C%5C%20y%27%5Fi%5C%5C%201%20%5Cend%7Bbmatrix%7D%20%5Csim%20H%20%20%5Cbegin%7Bbmatrix%7D%20x%5Fi%5C%5C%20y%5Fi%5C%5C%201%20%5Cend%7Bbmatrix%7D)
	///
	/// so that the back-projection error
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Csum%20%5Fi%20%5Cleft%20%28%20x%27%5Fi%2D%20%5Cfrac%7Bh%5F%7B11%7D%20x%5Fi%20%2B%20h%5F%7B12%7D%20y%5Fi%20%2B%20h%5F%7B13%7D%7D%7Bh%5F%7B31%7D%20x%5Fi%20%2B%20h%5F%7B32%7D%20y%5Fi%20%2B%20h%5F%7B33%7D%7D%20%5Cright%20%29%5E2%2B%20%5Cleft%20%28%20y%27%5Fi%2D%20%5Cfrac%7Bh%5F%7B21%7D%20x%5Fi%20%2B%20h%5F%7B22%7D%20y%5Fi%20%2B%20h%5F%7B23%7D%7D%7Bh%5F%7B31%7D%20x%5Fi%20%2B%20h%5F%7B32%7D%20y%5Fi%20%2B%20h%5F%7B33%7D%7D%20%5Cright%20%29%5E2)
	///
	/// is minimized. If the parameter method is set to the default value 0, the function uses all the point
	/// pairs to compute an initial homography estimate with a simple least-squares scheme.
	///
	/// However, if not all of the point pairs ( ![inline formula](https://latex.codecogs.com/png.latex?srcPoints%5Fi), ![inline formula](https://latex.codecogs.com/png.latex?dstPoints%5Fi) ) fit the rigid perspective
	/// transformation (that is, there are some outliers), this initial estimate will be poor. In this case,
	/// you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different
	/// random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix
	/// using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the
	/// computed homography (which is the number of inliers for RANSAC or the least median re-projection error for
	/// LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and
	/// the mask of inliers/outliers.
	///
	/// Regardless of the method, robust or not, the computed homography matrix is refined further (using
	/// inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the
	/// re-projection error even more.
	///
	/// The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to
	/// distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
	/// correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the
	/// noise is rather small, use the default method (method=0).
	///
	/// The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is
	/// determined up to a scale. If ![inline formula](https://latex.codecogs.com/png.latex?h%5F%7B33%7D) is non-zero, the matrix is normalized so that ![inline formula](https://latex.codecogs.com/png.latex?h%5F%7B33%7D%3D1).
	///
	/// Note: Whenever an ![inline formula](https://latex.codecogs.com/png.latex?H) matrix cannot be estimated, an empty one will be returned.
	/// ## See also
	/// getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective,
	/// perspectiveTransform
	///
	/// ## Note
	/// This alternative version of [find_homography_ext] function uses the following default values for its arguments:
	/// * method: 0
	/// * ransac_reproj_threshold: 3
	/// * mask: noArray()
	/// * max_iters: 2000
	/// * confidence: 0.995
	#[inline]
	pub fn find_homography_ext_def(src_points: &impl ToInputArray, dst_points: &impl ToInputArray) -> Result<core::Mat> {
		input_array_arg!(src_points);
		input_array_arg!(dst_points);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findHomography_const__InputArrayR_const__InputArrayR(src_points.as_raw__InputArray(), dst_points.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Finds a perspective transformation between two planes.
	///
	/// ## Parameters
	/// * srcPoints: Coordinates of the points in the original plane, a matrix of the type CV_32FC2
	/// or vector\<Point2f\> .
	/// * dstPoints: Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or
	/// a vector\<Point2f\> .
	/// * method: Method used to compute a homography matrix. The following methods are possible:
	/// *   **0** - a regular method using all the points, i.e., the least squares method
	/// *   [RANSAC] - RANSAC-based robust method
	/// *   [LMEDS] - Least-Median robust method
	/// *   [RHO] - PROSAC-based robust method
	/// * ransacReprojThreshold: Maximum allowed reprojection error to treat a point pair as an inlier
	/// (used in the RANSAC and RHO methods only). That is, if
	/// ![block formula](https://latex.codecogs.com/png.latex?%5C%7C%20%5Ctexttt%7BdstPoints%7D%20%5Fi%20%2D%20%20%5Ctexttt%7BconvertPointsHomogeneous%7D%20%28%20%5Ctexttt%7BH%7D%20%5Ccdot%20%5Ctexttt%7BsrcPoints%7D%20%5Fi%29%20%5C%7C%5F2%20%20%3E%20%20%5Ctexttt%7BransacReprojThreshold%7D)
	/// then the point ![inline formula](https://latex.codecogs.com/png.latex?i) is considered as an outlier. If srcPoints and dstPoints are measured in pixels,
	/// it usually makes sense to set this parameter somewhere in the range of 1 to 10.
	/// * mask: Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input
	/// mask values are ignored.
	/// * maxIters: The maximum number of RANSAC iterations.
	/// * confidence: Confidence level, between 0 and 1.
	///
	/// The function finds and returns the perspective transformation ![inline formula](https://latex.codecogs.com/png.latex?H) between the source and the
	/// destination planes:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?s%5Fi%20%20%5Cbegin%7Bbmatrix%7D%20x%27%5Fi%5C%5C%20y%27%5Fi%5C%5C%201%20%5Cend%7Bbmatrix%7D%20%5Csim%20H%20%20%5Cbegin%7Bbmatrix%7D%20x%5Fi%5C%5C%20y%5Fi%5C%5C%201%20%5Cend%7Bbmatrix%7D)
	///
	/// so that the back-projection error
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Csum%20%5Fi%20%5Cleft%20%28%20x%27%5Fi%2D%20%5Cfrac%7Bh%5F%7B11%7D%20x%5Fi%20%2B%20h%5F%7B12%7D%20y%5Fi%20%2B%20h%5F%7B13%7D%7D%7Bh%5F%7B31%7D%20x%5Fi%20%2B%20h%5F%7B32%7D%20y%5Fi%20%2B%20h%5F%7B33%7D%7D%20%5Cright%20%29%5E2%2B%20%5Cleft%20%28%20y%27%5Fi%2D%20%5Cfrac%7Bh%5F%7B21%7D%20x%5Fi%20%2B%20h%5F%7B22%7D%20y%5Fi%20%2B%20h%5F%7B23%7D%7D%7Bh%5F%7B31%7D%20x%5Fi%20%2B%20h%5F%7B32%7D%20y%5Fi%20%2B%20h%5F%7B33%7D%7D%20%5Cright%20%29%5E2)
	///
	/// is minimized. If the parameter method is set to the default value 0, the function uses all the point
	/// pairs to compute an initial homography estimate with a simple least-squares scheme.
	///
	/// However, if not all of the point pairs ( ![inline formula](https://latex.codecogs.com/png.latex?srcPoints%5Fi), ![inline formula](https://latex.codecogs.com/png.latex?dstPoints%5Fi) ) fit the rigid perspective
	/// transformation (that is, there are some outliers), this initial estimate will be poor. In this case,
	/// you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different
	/// random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix
	/// using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the
	/// computed homography (which is the number of inliers for RANSAC or the least median re-projection error for
	/// LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and
	/// the mask of inliers/outliers.
	///
	/// Regardless of the method, robust or not, the computed homography matrix is refined further (using
	/// inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the
	/// re-projection error even more.
	///
	/// The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to
	/// distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
	/// correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the
	/// noise is rather small, use the default method (method=0).
	///
	/// The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is
	/// determined up to a scale. If ![inline formula](https://latex.codecogs.com/png.latex?h%5F%7B33%7D) is non-zero, the matrix is normalized so that ![inline formula](https://latex.codecogs.com/png.latex?h%5F%7B33%7D%3D1).
	///
	/// Note: Whenever an ![inline formula](https://latex.codecogs.com/png.latex?H) matrix cannot be estimated, an empty one will be returned.
	/// ## See also
	/// getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective,
	/// perspectiveTransform
	///
	/// ## Overloaded parameters
	///
	///
	/// ## Note
	/// This alternative version of [find_homography] function uses the following default values for its arguments:
	/// * method: 0
	/// * ransac_reproj_threshold: 3
	#[inline]
	pub fn find_homography_def(src_points: &impl ToInputArray, dst_points: &impl ToInputArray, mask: &mut impl ToOutputArray) -> Result<core::Mat> {
		input_array_arg!(src_points);
		input_array_arg!(dst_points);
		output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findHomography_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src_points.as_raw__InputArray(), dst_points.as_raw__InputArray(), mask.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	#[inline]
	pub fn find_homography_1(src_points: &impl ToInputArray, dst_points: &impl ToInputArray, mask: &mut impl ToOutputArray, params: crate::calib3d::UsacParams) -> Result<core::Mat> {
		input_array_arg!(src_points);
		input_array_arg!(dst_points);
		output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findHomography_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const_UsacParamsR(src_points.as_raw__InputArray(), dst_points.as_raw__InputArray(), mask.as_raw__OutputArray(), &params, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Finds a perspective transformation between two planes.
	///
	/// ## Parameters
	/// * srcPoints: Coordinates of the points in the original plane, a matrix of the type CV_32FC2
	/// or vector\<Point2f\> .
	/// * dstPoints: Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or
	/// a vector\<Point2f\> .
	/// * method: Method used to compute a homography matrix. The following methods are possible:
	/// *   **0** - a regular method using all the points, i.e., the least squares method
	/// *   [RANSAC] - RANSAC-based robust method
	/// *   [LMEDS] - Least-Median robust method
	/// *   [RHO] - PROSAC-based robust method
	/// * ransacReprojThreshold: Maximum allowed reprojection error to treat a point pair as an inlier
	/// (used in the RANSAC and RHO methods only). That is, if
	/// ![block formula](https://latex.codecogs.com/png.latex?%5C%7C%20%5Ctexttt%7BdstPoints%7D%20%5Fi%20%2D%20%20%5Ctexttt%7BconvertPointsHomogeneous%7D%20%28%20%5Ctexttt%7BH%7D%20%5Ccdot%20%5Ctexttt%7BsrcPoints%7D%20%5Fi%29%20%5C%7C%5F2%20%20%3E%20%20%5Ctexttt%7BransacReprojThreshold%7D)
	/// then the point ![inline formula](https://latex.codecogs.com/png.latex?i) is considered as an outlier. If srcPoints and dstPoints are measured in pixels,
	/// it usually makes sense to set this parameter somewhere in the range of 1 to 10.
	/// * mask: Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input
	/// mask values are ignored.
	/// * maxIters: The maximum number of RANSAC iterations.
	/// * confidence: Confidence level, between 0 and 1.
	///
	/// The function finds and returns the perspective transformation ![inline formula](https://latex.codecogs.com/png.latex?H) between the source and the
	/// destination planes:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?s%5Fi%20%20%5Cbegin%7Bbmatrix%7D%20x%27%5Fi%5C%5C%20y%27%5Fi%5C%5C%201%20%5Cend%7Bbmatrix%7D%20%5Csim%20H%20%20%5Cbegin%7Bbmatrix%7D%20x%5Fi%5C%5C%20y%5Fi%5C%5C%201%20%5Cend%7Bbmatrix%7D)
	///
	/// so that the back-projection error
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Csum%20%5Fi%20%5Cleft%20%28%20x%27%5Fi%2D%20%5Cfrac%7Bh%5F%7B11%7D%20x%5Fi%20%2B%20h%5F%7B12%7D%20y%5Fi%20%2B%20h%5F%7B13%7D%7D%7Bh%5F%7B31%7D%20x%5Fi%20%2B%20h%5F%7B32%7D%20y%5Fi%20%2B%20h%5F%7B33%7D%7D%20%5Cright%20%29%5E2%2B%20%5Cleft%20%28%20y%27%5Fi%2D%20%5Cfrac%7Bh%5F%7B21%7D%20x%5Fi%20%2B%20h%5F%7B22%7D%20y%5Fi%20%2B%20h%5F%7B23%7D%7D%7Bh%5F%7B31%7D%20x%5Fi%20%2B%20h%5F%7B32%7D%20y%5Fi%20%2B%20h%5F%7B33%7D%7D%20%5Cright%20%29%5E2)
	///
	/// is minimized. If the parameter method is set to the default value 0, the function uses all the point
	/// pairs to compute an initial homography estimate with a simple least-squares scheme.
	///
	/// However, if not all of the point pairs ( ![inline formula](https://latex.codecogs.com/png.latex?srcPoints%5Fi), ![inline formula](https://latex.codecogs.com/png.latex?dstPoints%5Fi) ) fit the rigid perspective
	/// transformation (that is, there are some outliers), this initial estimate will be poor. In this case,
	/// you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different
	/// random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix
	/// using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the
	/// computed homography (which is the number of inliers for RANSAC or the least median re-projection error for
	/// LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and
	/// the mask of inliers/outliers.
	///
	/// Regardless of the method, robust or not, the computed homography matrix is refined further (using
	/// inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the
	/// re-projection error even more.
	///
	/// The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to
	/// distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
	/// correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the
	/// noise is rather small, use the default method (method=0).
	///
	/// The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is
	/// determined up to a scale. If ![inline formula](https://latex.codecogs.com/png.latex?h%5F%7B33%7D) is non-zero, the matrix is normalized so that ![inline formula](https://latex.codecogs.com/png.latex?h%5F%7B33%7D%3D1).
	///
	/// Note: Whenever an ![inline formula](https://latex.codecogs.com/png.latex?H) matrix cannot be estimated, an empty one will be returned.
	/// ## See also
	/// getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective,
	/// perspectiveTransform
	///
	/// ## Overloaded parameters
	///
	/// ## C++ default parameters
	/// * method: 0
	/// * ransac_reproj_threshold: 3
	#[inline]
	pub fn find_homography(src_points: &impl ToInputArray, dst_points: &impl ToInputArray, mask: &mut impl ToOutputArray, method: i32, ransac_reproj_threshold: f64) -> Result<core::Mat> {
		input_array_arg!(src_points);
		input_array_arg!(dst_points);
		output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findHomography_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_double(src_points.as_raw__InputArray(), dst_points.as_raw__InputArray(), mask.as_raw__OutputArray(), method, ransac_reproj_threshold, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Finds a perspective transformation between two planes.
	///
	/// ## Parameters
	/// * srcPoints: Coordinates of the points in the original plane, a matrix of the type CV_32FC2
	/// or vector\<Point2f\> .
	/// * dstPoints: Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or
	/// a vector\<Point2f\> .
	/// * method: Method used to compute a homography matrix. The following methods are possible:
	/// *   **0** - a regular method using all the points, i.e., the least squares method
	/// *   [RANSAC] - RANSAC-based robust method
	/// *   [LMEDS] - Least-Median robust method
	/// *   [RHO] - PROSAC-based robust method
	/// * ransacReprojThreshold: Maximum allowed reprojection error to treat a point pair as an inlier
	/// (used in the RANSAC and RHO methods only). That is, if
	/// ![block formula](https://latex.codecogs.com/png.latex?%5C%7C%20%5Ctexttt%7BdstPoints%7D%20%5Fi%20%2D%20%20%5Ctexttt%7BconvertPointsHomogeneous%7D%20%28%20%5Ctexttt%7BH%7D%20%5Ccdot%20%5Ctexttt%7BsrcPoints%7D%20%5Fi%29%20%5C%7C%5F2%20%20%3E%20%20%5Ctexttt%7BransacReprojThreshold%7D)
	/// then the point ![inline formula](https://latex.codecogs.com/png.latex?i) is considered as an outlier. If srcPoints and dstPoints are measured in pixels,
	/// it usually makes sense to set this parameter somewhere in the range of 1 to 10.
	/// * mask: Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input
	/// mask values are ignored.
	/// * maxIters: The maximum number of RANSAC iterations.
	/// * confidence: Confidence level, between 0 and 1.
	///
	/// The function finds and returns the perspective transformation ![inline formula](https://latex.codecogs.com/png.latex?H) between the source and the
	/// destination planes:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?s%5Fi%20%20%5Cbegin%7Bbmatrix%7D%20x%27%5Fi%5C%5C%20y%27%5Fi%5C%5C%201%20%5Cend%7Bbmatrix%7D%20%5Csim%20H%20%20%5Cbegin%7Bbmatrix%7D%20x%5Fi%5C%5C%20y%5Fi%5C%5C%201%20%5Cend%7Bbmatrix%7D)
	///
	/// so that the back-projection error
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Csum%20%5Fi%20%5Cleft%20%28%20x%27%5Fi%2D%20%5Cfrac%7Bh%5F%7B11%7D%20x%5Fi%20%2B%20h%5F%7B12%7D%20y%5Fi%20%2B%20h%5F%7B13%7D%7D%7Bh%5F%7B31%7D%20x%5Fi%20%2B%20h%5F%7B32%7D%20y%5Fi%20%2B%20h%5F%7B33%7D%7D%20%5Cright%20%29%5E2%2B%20%5Cleft%20%28%20y%27%5Fi%2D%20%5Cfrac%7Bh%5F%7B21%7D%20x%5Fi%20%2B%20h%5F%7B22%7D%20y%5Fi%20%2B%20h%5F%7B23%7D%7D%7Bh%5F%7B31%7D%20x%5Fi%20%2B%20h%5F%7B32%7D%20y%5Fi%20%2B%20h%5F%7B33%7D%7D%20%5Cright%20%29%5E2)
	///
	/// is minimized. If the parameter method is set to the default value 0, the function uses all the point
	/// pairs to compute an initial homography estimate with a simple least-squares scheme.
	///
	/// However, if not all of the point pairs ( ![inline formula](https://latex.codecogs.com/png.latex?srcPoints%5Fi), ![inline formula](https://latex.codecogs.com/png.latex?dstPoints%5Fi) ) fit the rigid perspective
	/// transformation (that is, there are some outliers), this initial estimate will be poor. In this case,
	/// you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different
	/// random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix
	/// using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the
	/// computed homography (which is the number of inliers for RANSAC or the least median re-projection error for
	/// LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and
	/// the mask of inliers/outliers.
	///
	/// Regardless of the method, robust or not, the computed homography matrix is refined further (using
	/// inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the
	/// re-projection error even more.
	///
	/// The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to
	/// distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
	/// correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the
	/// noise is rather small, use the default method (method=0).
	///
	/// The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is
	/// determined up to a scale. If ![inline formula](https://latex.codecogs.com/png.latex?h%5F%7B33%7D) is non-zero, the matrix is normalized so that ![inline formula](https://latex.codecogs.com/png.latex?h%5F%7B33%7D%3D1).
	///
	/// Note: Whenever an ![inline formula](https://latex.codecogs.com/png.latex?H) matrix cannot be estimated, an empty one will be returned.
	/// ## See also
	/// getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective,
	/// perspectiveTransform
	///
	/// ## C++ default parameters
	/// * method: 0
	/// * ransac_reproj_threshold: 3
	/// * mask: noArray()
	/// * max_iters: 2000
	/// * confidence: 0.995
	#[inline]
	pub fn find_homography_ext(src_points: &impl ToInputArray, dst_points: &impl ToInputArray, method: i32, ransac_reproj_threshold: f64, mask: &mut impl ToOutputArray, max_iters: i32, confidence: f64) -> Result<core::Mat> {
		input_array_arg!(src_points);
		input_array_arg!(dst_points);
		output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_findHomography_const__InputArrayR_const__InputArrayR_int_double_const__OutputArrayR_const_int_const_double(src_points.as_raw__InputArray(), dst_points.as_raw__InputArray(), method, ransac_reproj_threshold, mask.as_raw__OutputArray(), max_iters, confidence, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Performs camera calibration
	///
	/// ## Parameters
	/// * objectPoints: vector of vectors of calibration pattern points in the calibration pattern
	///    coordinate space.
	/// * imagePoints: vector of vectors of the projections of calibration pattern points.
	///    imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to
	///    objectPoints[i].size() for each i.
	/// * image_size: Size of the image used only to initialize the camera intrinsic matrix.
	/// * K: Output 3x3 floating-point camera intrinsic matrix
	///    ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) . If
	///    [fisheye::CALIB_USE_INTRINSIC_GUESS] is specified, some or all of fx, fy, cx, cy must be
	///    initialized before calling the function.
	/// * D: Output vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * rvecs: Output vector of rotation vectors (see [Rodrigues] ) estimated for each pattern view.
	///    That is, each k-th rotation vector together with the corresponding k-th translation vector (see
	///    the next output parameter description) brings the calibration pattern from the model coordinate
	///    space (in which object points are specified) to the world coordinate space, that is, a real
	///    position of the calibration pattern in the k-th pattern view (k=0.. *M* -1).
	/// * tvecs: Output vector of translation vectors estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of the following values:
	///    *    [fisheye::CALIB_USE_INTRINSIC_GUESS]  cameraMatrix contains valid initial values of
	///    fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
	///    center ( imageSize is used), and focal distances are computed in a least-squares fashion.
	///    *    [fisheye::CALIB_RECOMPUTE_EXTRINSIC]  Extrinsic will be recomputed after each iteration
	///    of intrinsic optimization.
	///    *    [fisheye::CALIB_CHECK_COND]  The functions will check validity of condition number.
	///    *    [fisheye::CALIB_FIX_SKEW]  Skew coefficient (alpha) is set to zero and stay zero.
	///    *    [fisheye::CALIB_FIX_K1],..., [fisheye::CALIB_FIX_K4] Selected distortion coefficients
	///    are set to zeros and stay zero.
	///    *    [fisheye::CALIB_FIX_PRINCIPAL_POINT]  The principal point is not changed during the global
	/// optimization. It stays at the center or at a different location specified when [fisheye::CALIB_USE_INTRINSIC_GUESS] is set too.
	///    *    [fisheye::CALIB_FIX_FOCAL_LENGTH] The focal length is not changed during the global
	/// optimization. It is the ![inline formula](https://latex.codecogs.com/png.latex?max%28width%2Cheight%29%2F%5Cpi) or the provided ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx), ![inline formula](https://latex.codecogs.com/png.latex?f%5Fy) when [fisheye::CALIB_USE_INTRINSIC_GUESS] is set too.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// ## Note
	/// This alternative version of [calibrate] function uses the following default values for its arguments:
	/// * flags: 0
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,100,DBL_EPSILON)
	#[inline]
	pub fn calibrate_def(object_points: &impl ToInputArray, image_points: &impl ToInputArray, image_size: core::Size, k: &mut impl ToInputOutputArray, d: &mut impl ToInputOutputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_output_array_arg!(k);
		input_output_array_arg!(d);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_calibrate_const__InputArrayR_const__InputArrayR_const_SizeR_const__InputOutputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), &image_size, k.as_raw__InputOutputArray(), d.as_raw__InputOutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Performs camera calibration
	///
	/// ## Parameters
	/// * objectPoints: vector of vectors of calibration pattern points in the calibration pattern
	///    coordinate space.
	/// * imagePoints: vector of vectors of the projections of calibration pattern points.
	///    imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to
	///    objectPoints[i].size() for each i.
	/// * image_size: Size of the image used only to initialize the camera intrinsic matrix.
	/// * K: Output 3x3 floating-point camera intrinsic matrix
	///    ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) . If
	///    [fisheye::CALIB_USE_INTRINSIC_GUESS] is specified, some or all of fx, fy, cx, cy must be
	///    initialized before calling the function.
	/// * D: Output vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * rvecs: Output vector of rotation vectors (see [Rodrigues] ) estimated for each pattern view.
	///    That is, each k-th rotation vector together with the corresponding k-th translation vector (see
	///    the next output parameter description) brings the calibration pattern from the model coordinate
	///    space (in which object points are specified) to the world coordinate space, that is, a real
	///    position of the calibration pattern in the k-th pattern view (k=0.. *M* -1).
	/// * tvecs: Output vector of translation vectors estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of the following values:
	///    *    [fisheye::CALIB_USE_INTRINSIC_GUESS]  cameraMatrix contains valid initial values of
	///    fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
	///    center ( imageSize is used), and focal distances are computed in a least-squares fashion.
	///    *    [fisheye::CALIB_RECOMPUTE_EXTRINSIC]  Extrinsic will be recomputed after each iteration
	///    of intrinsic optimization.
	///    *    [fisheye::CALIB_CHECK_COND]  The functions will check validity of condition number.
	///    *    [fisheye::CALIB_FIX_SKEW]  Skew coefficient (alpha) is set to zero and stay zero.
	///    *    [fisheye::CALIB_FIX_K1],..., [fisheye::CALIB_FIX_K4] Selected distortion coefficients
	///    are set to zeros and stay zero.
	///    *    [fisheye::CALIB_FIX_PRINCIPAL_POINT]  The principal point is not changed during the global
	/// optimization. It stays at the center or at a different location specified when [fisheye::CALIB_USE_INTRINSIC_GUESS] is set too.
	///    *    [fisheye::CALIB_FIX_FOCAL_LENGTH] The focal length is not changed during the global
	/// optimization. It is the ![inline formula](https://latex.codecogs.com/png.latex?max%28width%2Cheight%29%2F%5Cpi) or the provided ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx), ![inline formula](https://latex.codecogs.com/png.latex?f%5Fy) when [fisheye::CALIB_USE_INTRINSIC_GUESS] is set too.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// ## C++ default parameters
	/// * flags: 0
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,100,DBL_EPSILON)
	#[inline]
	pub fn calibrate(object_points: &impl ToInputArray, image_points: &impl ToInputArray, image_size: core::Size, k: &mut impl ToInputOutputArray, d: &mut impl ToInputOutputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray, flags: i32, criteria: core::TermCriteria) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_output_array_arg!(k);
		input_output_array_arg!(d);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_calibrate_const__InputArrayR_const__InputArrayR_const_SizeR_const__InputOutputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_int_TermCriteria(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), &image_size, k.as_raw__InputOutputArray(), d.as_raw__InputOutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), flags, &criteria, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Distorts 2D points using fisheye model.
	///
	/// ## Parameters
	/// * undistorted: Array of object points, 1xN/Nx1 2-channel (or vector\<Point2f\> ), where N is
	/// the number of points in the view.
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * alpha: The skew coefficient.
	/// * distorted: Output array of image points, 1xN/Nx1 2-channel, or vector\<Point2f\> .
	///
	/// Note that the function assumes the camera intrinsic matrix of the undistorted points to be identity.
	/// This means if you want to distort image points you have to multiply them with ![inline formula](https://latex.codecogs.com/png.latex?K%5E%7B%2D1%7D) or
	/// use another function overload.
	///
	/// ## Note
	/// This alternative version of [fisheye_distort_points] function uses the following default values for its arguments:
	/// * alpha: 0
	#[inline]
	pub fn fisheye_distort_points_def(undistorted: &impl ToInputArray, distorted: &mut impl ToOutputArray, k: &impl ToInputArray, d: &impl ToInputArray) -> Result<()> {
		input_array_arg!(undistorted);
		output_array_arg!(distorted);
		input_array_arg!(k);
		input_array_arg!(d);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_distortPoints_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR(undistorted.as_raw__InputArray(), distorted.as_raw__OutputArray(), k.as_raw__InputArray(), d.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Distorts 2D points using fisheye model.
	///
	/// ## Parameters
	/// * undistorted: Array of object points, 1xN/Nx1 2-channel (or vector\<Point2f\> ), where N is
	/// the number of points in the view.
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * alpha: The skew coefficient.
	/// * distorted: Output array of image points, 1xN/Nx1 2-channel, or vector\<Point2f\> .
	///
	/// Note that the function assumes the camera intrinsic matrix of the undistorted points to be identity.
	/// This means if you want to distort image points you have to multiply them with ![inline formula](https://latex.codecogs.com/png.latex?K%5E%7B%2D1%7D) or
	/// use another function overload.
	///
	/// ## Overloaded parameters
	///
	/// Overload of distortPoints function to handle cases when undistorted points are obtained with non-identity
	/// camera matrix, e.g. output of #estimateNewCameraMatrixForUndistortRectify.
	/// * undistorted: Array of object points, 1xN/Nx1 2-channel (or vector\<Point2f\> ), where N is
	/// the number of points in the view.
	/// * Kundistorted: Camera intrinsic matrix used as new camera matrix for undistortion.
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * alpha: The skew coefficient.
	/// * distorted: Output array of image points, 1xN/Nx1 2-channel, or vector\<Point2f\> .
	/// ## See also
	/// estimateNewCameraMatrixForUndistortRectify
	///
	/// ## Note
	/// This alternative version of [distort_points] function uses the following default values for its arguments:
	/// * alpha: 0
	#[inline]
	pub fn distort_points_def(undistorted: &impl ToInputArray, distorted: &mut impl ToOutputArray, kundistorted: &impl ToInputArray, k: &impl ToInputArray, d: &impl ToInputArray) -> Result<()> {
		input_array_arg!(undistorted);
		output_array_arg!(distorted);
		input_array_arg!(kundistorted);
		input_array_arg!(k);
		input_array_arg!(d);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_distortPoints_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR(undistorted.as_raw__InputArray(), distorted.as_raw__OutputArray(), kundistorted.as_raw__InputArray(), k.as_raw__InputArray(), d.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Distorts 2D points using fisheye model.
	///
	/// ## Parameters
	/// * undistorted: Array of object points, 1xN/Nx1 2-channel (or vector\<Point2f\> ), where N is
	/// the number of points in the view.
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * alpha: The skew coefficient.
	/// * distorted: Output array of image points, 1xN/Nx1 2-channel, or vector\<Point2f\> .
	///
	/// Note that the function assumes the camera intrinsic matrix of the undistorted points to be identity.
	/// This means if you want to distort image points you have to multiply them with ![inline formula](https://latex.codecogs.com/png.latex?K%5E%7B%2D1%7D) or
	/// use another function overload.
	///
	/// ## Overloaded parameters
	///
	/// Overload of distortPoints function to handle cases when undistorted points are obtained with non-identity
	/// camera matrix, e.g. output of #estimateNewCameraMatrixForUndistortRectify.
	/// * undistorted: Array of object points, 1xN/Nx1 2-channel (or vector\<Point2f\> ), where N is
	/// the number of points in the view.
	/// * Kundistorted: Camera intrinsic matrix used as new camera matrix for undistortion.
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * alpha: The skew coefficient.
	/// * distorted: Output array of image points, 1xN/Nx1 2-channel, or vector\<Point2f\> .
	/// ## See also
	/// estimateNewCameraMatrixForUndistortRectify
	///
	/// ## C++ default parameters
	/// * alpha: 0
	#[inline]
	pub fn distort_points(undistorted: &impl ToInputArray, distorted: &mut impl ToOutputArray, kundistorted: &impl ToInputArray, k: &impl ToInputArray, d: &impl ToInputArray, alpha: f64) -> Result<()> {
		input_array_arg!(undistorted);
		output_array_arg!(distorted);
		input_array_arg!(kundistorted);
		input_array_arg!(k);
		input_array_arg!(d);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_distortPoints_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_double(undistorted.as_raw__InputArray(), distorted.as_raw__OutputArray(), kundistorted.as_raw__InputArray(), k.as_raw__InputArray(), d.as_raw__InputArray(), alpha, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Distorts 2D points using fisheye model.
	///
	/// ## Parameters
	/// * undistorted: Array of object points, 1xN/Nx1 2-channel (or vector\<Point2f\> ), where N is
	/// the number of points in the view.
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * alpha: The skew coefficient.
	/// * distorted: Output array of image points, 1xN/Nx1 2-channel, or vector\<Point2f\> .
	///
	/// Note that the function assumes the camera intrinsic matrix of the undistorted points to be identity.
	/// This means if you want to distort image points you have to multiply them with ![inline formula](https://latex.codecogs.com/png.latex?K%5E%7B%2D1%7D) or
	/// use another function overload.
	///
	/// ## C++ default parameters
	/// * alpha: 0
	#[inline]
	pub fn fisheye_distort_points(undistorted: &impl ToInputArray, distorted: &mut impl ToOutputArray, k: &impl ToInputArray, d: &impl ToInputArray, alpha: f64) -> Result<()> {
		input_array_arg!(undistorted);
		output_array_arg!(distorted);
		input_array_arg!(k);
		input_array_arg!(d);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_distortPoints_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_double(undistorted.as_raw__InputArray(), distorted.as_raw__OutputArray(), k.as_raw__InputArray(), d.as_raw__InputArray(), alpha, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Estimates new camera intrinsic matrix for undistortion or rectification.
	///
	/// ## Parameters
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * image_size: Size of the image
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * R: Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3
	/// 1-channel or 1x1 3-channel
	/// * P: New camera intrinsic matrix (3x3) or new projection matrix (3x4)
	/// * balance: Sets the new focal length in range between the min focal length and the max focal
	/// length. Balance is in range of [0, 1].
	/// * new_size: the new size
	/// * fov_scale: Divisor for new focal length.
	///
	/// ## Note
	/// This alternative version of [estimate_new_camera_matrix_for_undistort_rectify] function uses the following default values for its arguments:
	/// * balance: 0.0
	/// * new_size: Size()
	/// * fov_scale: 1.0
	#[inline]
	pub fn estimate_new_camera_matrix_for_undistort_rectify_def(k: &impl ToInputArray, d: &impl ToInputArray, image_size: core::Size, r: &impl ToInputArray, p: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(k);
		input_array_arg!(d);
		input_array_arg!(r);
		output_array_arg!(p);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_estimateNewCameraMatrixForUndistortRectify_const__InputArrayR_const__InputArrayR_const_SizeR_const__InputArrayR_const__OutputArrayR(k.as_raw__InputArray(), d.as_raw__InputArray(), &image_size, r.as_raw__InputArray(), p.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Estimates new camera intrinsic matrix for undistortion or rectification.
	///
	/// ## Parameters
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * image_size: Size of the image
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * R: Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3
	/// 1-channel or 1x1 3-channel
	/// * P: New camera intrinsic matrix (3x3) or new projection matrix (3x4)
	/// * balance: Sets the new focal length in range between the min focal length and the max focal
	/// length. Balance is in range of [0, 1].
	/// * new_size: the new size
	/// * fov_scale: Divisor for new focal length.
	///
	/// ## C++ default parameters
	/// * balance: 0.0
	/// * new_size: Size()
	/// * fov_scale: 1.0
	#[inline]
	pub fn estimate_new_camera_matrix_for_undistort_rectify(k: &impl ToInputArray, d: &impl ToInputArray, image_size: core::Size, r: &impl ToInputArray, p: &mut impl ToOutputArray, balance: f64, new_size: core::Size, fov_scale: f64) -> Result<()> {
		input_array_arg!(k);
		input_array_arg!(d);
		input_array_arg!(r);
		output_array_arg!(p);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_estimateNewCameraMatrixForUndistortRectify_const__InputArrayR_const__InputArrayR_const_SizeR_const__InputArrayR_const__OutputArrayR_double_const_SizeR_double(k.as_raw__InputArray(), d.as_raw__InputArray(), &image_size, r.as_raw__InputArray(), p.as_raw__OutputArray(), balance, &new_size, fov_scale, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes undistortion and rectification maps for image transform by #remap. If D is empty zero
	/// distortion is used, if R or P is empty identity matrixes are used.
	///
	/// ## Parameters
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * R: Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3
	/// 1-channel or 1x1 3-channel
	/// * P: New camera intrinsic matrix (3x3) or new projection matrix (3x4)
	/// * size: Undistorted image size.
	/// * m1type: Type of the first output map that can be CV_32FC1 or CV_16SC2 . See [convert_maps]
	/// for details.
	/// * map1: The first output map.
	/// * map2: The second output map.
	#[inline]
	pub fn fisheye_init_undistort_rectify_map(k: &impl ToInputArray, d: &impl ToInputArray, r: &impl ToInputArray, p: &impl ToInputArray, size: core::Size, m1type: i32, map1: &mut impl ToOutputArray, map2: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(k);
		input_array_arg!(d);
		input_array_arg!(r);
		input_array_arg!(p);
		output_array_arg!(map1);
		output_array_arg!(map2);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_initUndistortRectifyMap_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const_SizeR_int_const__OutputArrayR_const__OutputArrayR(k.as_raw__InputArray(), d.as_raw__InputArray(), r.as_raw__InputArray(), p.as_raw__InputArray(), &size, m1type, map1.as_raw__OutputArray(), map2.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Projects points using fisheye model
	///
	/// ## Parameters
	/// * objectPoints: Array of object points, 1xN/Nx1 3-channel (or vector\<Point3f\> ), where N is
	/// the number of points in the view.
	/// * imagePoints: Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or
	/// vector\<Point2f\>.
	/// * affine: 
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * alpha: The skew coefficient.
	/// * jacobian: Optional output 2Nx15 jacobian matrix of derivatives of image points with respect
	/// to components of the focal lengths, coordinates of the principal point, distortion coefficients,
	/// rotation vector, translation vector, and the skew. In the old interface different components of
	/// the jacobian are returned via different output parameters.
	///
	/// The function computes projections of 3D points to the image plane given intrinsic and extrinsic
	/// camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of
	/// image points coordinates (as functions of all the input parameters) with respect to the particular
	/// parameters, intrinsic and/or extrinsic.
	///
	/// ## Note
	/// This alternative version of [fisheye_project_points] function uses the following default values for its arguments:
	/// * alpha: 0
	/// * jacobian: noArray()
	#[inline]
	pub fn fisheye_project_points_def(object_points: &impl ToInputArray, image_points: &mut impl ToOutputArray, affine: core::Affine3d, k: &impl ToInputArray, d: &impl ToInputArray) -> Result<()> {
		input_array_arg!(object_points);
		output_array_arg!(image_points);
		input_array_arg!(k);
		input_array_arg!(d);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_projectPoints_const__InputArrayR_const__OutputArrayR_const_Affine3dR_const__InputArrayR_const__InputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__OutputArray(), &affine, k.as_raw__InputArray(), d.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Projects points using fisheye model
	///
	/// ## Parameters
	/// * objectPoints: Array of object points, 1xN/Nx1 3-channel (or vector\<Point3f\> ), where N is
	/// the number of points in the view.
	/// * imagePoints: Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or
	/// vector\<Point2f\>.
	/// * affine: 
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * alpha: The skew coefficient.
	/// * jacobian: Optional output 2Nx15 jacobian matrix of derivatives of image points with respect
	/// to components of the focal lengths, coordinates of the principal point, distortion coefficients,
	/// rotation vector, translation vector, and the skew. In the old interface different components of
	/// the jacobian are returned via different output parameters.
	///
	/// The function computes projections of 3D points to the image plane given intrinsic and extrinsic
	/// camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of
	/// image points coordinates (as functions of all the input parameters) with respect to the particular
	/// parameters, intrinsic and/or extrinsic.
	///
	/// ## C++ default parameters
	/// * alpha: 0
	/// * jacobian: noArray()
	#[inline]
	pub fn fisheye_project_points(object_points: &impl ToInputArray, image_points: &mut impl ToOutputArray, affine: core::Affine3d, k: &impl ToInputArray, d: &impl ToInputArray, alpha: f64, jacobian: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(object_points);
		output_array_arg!(image_points);
		input_array_arg!(k);
		input_array_arg!(d);
		output_array_arg!(jacobian);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_projectPoints_const__InputArrayR_const__OutputArrayR_const_Affine3dR_const__InputArrayR_const__InputArrayR_double_const__OutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__OutputArray(), &affine, k.as_raw__InputArray(), d.as_raw__InputArray(), alpha, jacobian.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Projects points using fisheye model
	///
	/// ## Parameters
	/// * objectPoints: Array of object points, 1xN/Nx1 3-channel (or vector\<Point3f\> ), where N is
	/// the number of points in the view.
	/// * imagePoints: Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or
	/// vector\<Point2f\>.
	/// * affine: 
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * alpha: The skew coefficient.
	/// * jacobian: Optional output 2Nx15 jacobian matrix of derivatives of image points with respect
	/// to components of the focal lengths, coordinates of the principal point, distortion coefficients,
	/// rotation vector, translation vector, and the skew. In the old interface different components of
	/// the jacobian are returned via different output parameters.
	///
	/// The function computes projections of 3D points to the image plane given intrinsic and extrinsic
	/// camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of
	/// image points coordinates (as functions of all the input parameters) with respect to the particular
	/// parameters, intrinsic and/or extrinsic.
	///
	/// ## Overloaded parameters
	///
	///
	/// ## Note
	/// This alternative version of [fisheye_project_points_vec] function uses the following default values for its arguments:
	/// * alpha: 0
	/// * jacobian: noArray()
	#[inline]
	pub fn fisheye_project_points_vec_def(object_points: &impl ToInputArray, image_points: &mut impl ToOutputArray, rvec: &impl ToInputArray, tvec: &impl ToInputArray, k: &impl ToInputArray, d: &impl ToInputArray) -> Result<()> {
		input_array_arg!(object_points);
		output_array_arg!(image_points);
		input_array_arg!(rvec);
		input_array_arg!(tvec);
		input_array_arg!(k);
		input_array_arg!(d);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_projectPoints_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__OutputArray(), rvec.as_raw__InputArray(), tvec.as_raw__InputArray(), k.as_raw__InputArray(), d.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Projects points using fisheye model
	///
	/// ## Parameters
	/// * objectPoints: Array of object points, 1xN/Nx1 3-channel (or vector\<Point3f\> ), where N is
	/// the number of points in the view.
	/// * imagePoints: Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or
	/// vector\<Point2f\>.
	/// * affine: 
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * alpha: The skew coefficient.
	/// * jacobian: Optional output 2Nx15 jacobian matrix of derivatives of image points with respect
	/// to components of the focal lengths, coordinates of the principal point, distortion coefficients,
	/// rotation vector, translation vector, and the skew. In the old interface different components of
	/// the jacobian are returned via different output parameters.
	///
	/// The function computes projections of 3D points to the image plane given intrinsic and extrinsic
	/// camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of
	/// image points coordinates (as functions of all the input parameters) with respect to the particular
	/// parameters, intrinsic and/or extrinsic.
	///
	/// ## Overloaded parameters
	///
	/// ## C++ default parameters
	/// * alpha: 0
	/// * jacobian: noArray()
	#[inline]
	pub fn fisheye_project_points_vec(object_points: &impl ToInputArray, image_points: &mut impl ToOutputArray, rvec: &impl ToInputArray, tvec: &impl ToInputArray, k: &impl ToInputArray, d: &impl ToInputArray, alpha: f64, jacobian: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(object_points);
		output_array_arg!(image_points);
		input_array_arg!(rvec);
		input_array_arg!(tvec);
		input_array_arg!(k);
		input_array_arg!(d);
		output_array_arg!(jacobian);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_projectPoints_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_double_const__OutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__OutputArray(), rvec.as_raw__InputArray(), tvec.as_raw__InputArray(), k.as_raw__InputArray(), d.as_raw__InputArray(), alpha, jacobian.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds an object pose from 3D-2D point correspondences using the RANSAC scheme for fisheye camera moodel.
	///
	/// ## Parameters
	/// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or
	/// 1xN/Nx1 3-channel, where N is the number of points. vector\<Point3d\> can be also passed here.
	/// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
	/// where N is the number of points. vector\<Point2d\> can be also passed here.
	/// * cameraMatrix: Input camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients (4x1/1x4).
	/// * rvec: Output rotation vector (see [Rodrigues] ) that, together with tvec, brings points from
	/// the model coordinate system to the camera coordinate system.
	/// * tvec: Output translation vector.
	/// * useExtrinsicGuess: Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
	/// the provided rvec and tvec values as initial approximations of the rotation and translation
	/// vectors, respectively, and further optimizes them.
	/// * iterationsCount: Number of iterations.
	/// * reprojectionError: Inlier threshold value used by the RANSAC procedure. The parameter value
	/// is the maximum allowed distance between the observed and computed point projections to consider it
	/// an inlier.
	/// * confidence: The probability that the algorithm produces a useful result.
	/// * inliers: Output vector that contains indices of inliers in objectPoints and imagePoints .
	/// * flags: Method for solving a PnP problem: see [calib3d_solvePnP_flags]
	/// This function returns the rotation and the translation vectors that transform a 3D point expressed in the object
	/// coordinate frame to the camera coordinate frame, using different methods:
	/// - P3P methods ([SOLVEPNP_P3P], [SOLVEPNP_AP3P]): need 4 input points to return a unique solution.
	/// - [SOLVEPNP_IPPE] Input points must be >= 4 and object points must be coplanar.
	/// - [SOLVEPNP_IPPE_SQUARE] Special case suitable for marker pose estimation.
	/// Number of input points must be 4. Object points must be defined in the following order:
	/// - point 0: [-squareLength / 2,  squareLength / 2, 0]
	/// - point 1: [ squareLength / 2,  squareLength / 2, 0]
	/// - point 2: [ squareLength / 2, -squareLength / 2, 0]
	/// - point 3: [-squareLength / 2, -squareLength / 2, 0]
	/// - for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
	/// * criteria: Termination criteria for internal undistortPoints call.
	/// The function interally undistorts points with [undistortPoints] and call [cv::solvePnP],
	/// thus the input are very similar. More information about Perspective-n-Points is described in [calib3d_solvePnP]
	/// for more information.
	///
	/// ## Note
	/// This alternative version of [solve_pnp_ransac_2] function uses the following default values for its arguments:
	/// * use_extrinsic_guess: false
	/// * iterations_count: 100
	/// * reprojection_error: 8.0
	/// * confidence: 0.99
	/// * inliers: noArray()
	/// * flags: SOLVEPNP_ITERATIVE
	/// * criteria: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,10,1e-8)
	#[inline]
	pub fn solve_pnp_ransac_2_def(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvec: &mut impl ToOutputArray, tvec: &mut impl ToOutputArray) -> Result<bool> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(rvec);
		output_array_arg!(tvec);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_solvePnPRansac_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__OutputArray(), tvec.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds an object pose from 3D-2D point correspondences using the RANSAC scheme for fisheye camera moodel.
	///
	/// ## Parameters
	/// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or
	/// 1xN/Nx1 3-channel, where N is the number of points. vector\<Point3d\> can be also passed here.
	/// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
	/// where N is the number of points. vector\<Point2d\> can be also passed here.
	/// * cameraMatrix: Input camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients (4x1/1x4).
	/// * rvec: Output rotation vector (see [Rodrigues] ) that, together with tvec, brings points from
	/// the model coordinate system to the camera coordinate system.
	/// * tvec: Output translation vector.
	/// * useExtrinsicGuess: Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
	/// the provided rvec and tvec values as initial approximations of the rotation and translation
	/// vectors, respectively, and further optimizes them.
	/// * iterationsCount: Number of iterations.
	/// * reprojectionError: Inlier threshold value used by the RANSAC procedure. The parameter value
	/// is the maximum allowed distance between the observed and computed point projections to consider it
	/// an inlier.
	/// * confidence: The probability that the algorithm produces a useful result.
	/// * inliers: Output vector that contains indices of inliers in objectPoints and imagePoints .
	/// * flags: Method for solving a PnP problem: see [calib3d_solvePnP_flags]
	/// This function returns the rotation and the translation vectors that transform a 3D point expressed in the object
	/// coordinate frame to the camera coordinate frame, using different methods:
	/// - P3P methods ([SOLVEPNP_P3P], [SOLVEPNP_AP3P]): need 4 input points to return a unique solution.
	/// - [SOLVEPNP_IPPE] Input points must be >= 4 and object points must be coplanar.
	/// - [SOLVEPNP_IPPE_SQUARE] Special case suitable for marker pose estimation.
	/// Number of input points must be 4. Object points must be defined in the following order:
	/// - point 0: [-squareLength / 2,  squareLength / 2, 0]
	/// - point 1: [ squareLength / 2,  squareLength / 2, 0]
	/// - point 2: [ squareLength / 2, -squareLength / 2, 0]
	/// - point 3: [-squareLength / 2, -squareLength / 2, 0]
	/// - for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
	/// * criteria: Termination criteria for internal undistortPoints call.
	/// The function interally undistorts points with [undistortPoints] and call [cv::solvePnP],
	/// thus the input are very similar. More information about Perspective-n-Points is described in [calib3d_solvePnP]
	/// for more information.
	///
	/// ## C++ default parameters
	/// * use_extrinsic_guess: false
	/// * iterations_count: 100
	/// * reprojection_error: 8.0
	/// * confidence: 0.99
	/// * inliers: noArray()
	/// * flags: SOLVEPNP_ITERATIVE
	/// * criteria: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,10,1e-8)
	#[inline]
	pub fn solve_pnp_ransac_2(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvec: &mut impl ToOutputArray, tvec: &mut impl ToOutputArray, use_extrinsic_guess: bool, iterations_count: i32, reprojection_error: f32, confidence: f64, inliers: &mut impl ToOutputArray, flags: i32, criteria: core::TermCriteria) -> Result<bool> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(rvec);
		output_array_arg!(tvec);
		output_array_arg!(inliers);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_solvePnPRansac_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_bool_int_float_double_const__OutputArrayR_int_TermCriteria(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__OutputArray(), tvec.as_raw__OutputArray(), use_extrinsic_guess, iterations_count, reprojection_error, confidence, inliers.as_raw__OutputArray(), flags, &criteria, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.
	///
	/// ## Parameters
	/// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or
	/// 1xN/Nx1 3-channel, where N is the number of points. vector\<Point3d\> can also be passed here.
	/// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
	/// where N is the number of points. vector\<Point2d\> can also be passed here.
	/// * cameraMatrix: Input camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients (4x1/1x4).
	/// * rvec: Output rotation vector (see [Rodrigues] ) that, together with tvec, brings points from
	/// the model coordinate system to the camera coordinate system.
	/// * tvec: Output translation vector.
	/// * useExtrinsicGuess: Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
	/// the provided rvec and tvec values as initial approximations of the rotation and translation
	/// vectors, respectively, and further optimizes them.
	/// * flags: Method for solving a PnP problem: see [calib3d_solvePnP_flags]
	/// * criteria: Termination criteria for internal undistortPoints call.
	/// The function interally undistorts points with [undistortPoints] and call [cv::solvePnP],
	/// thus the input are very similar. More information about Perspective-n-Points is described in [calib3d_solvePnP]
	/// for more information.
	///
	/// ## Note
	/// This alternative version of [solve_pnp_1] function uses the following default values for its arguments:
	/// * use_extrinsic_guess: false
	/// * flags: SOLVEPNP_ITERATIVE
	/// * criteria: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,10,1e-8)
	#[inline]
	pub fn solve_pnp_1_def(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvec: &mut impl ToOutputArray, tvec: &mut impl ToOutputArray) -> Result<bool> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(rvec);
		output_array_arg!(tvec);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_solvePnP_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__OutputArray(), tvec.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.
	///
	/// ## Parameters
	/// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or
	/// 1xN/Nx1 3-channel, where N is the number of points. vector\<Point3d\> can also be passed here.
	/// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
	/// where N is the number of points. vector\<Point2d\> can also be passed here.
	/// * cameraMatrix: Input camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients (4x1/1x4).
	/// * rvec: Output rotation vector (see [Rodrigues] ) that, together with tvec, brings points from
	/// the model coordinate system to the camera coordinate system.
	/// * tvec: Output translation vector.
	/// * useExtrinsicGuess: Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
	/// the provided rvec and tvec values as initial approximations of the rotation and translation
	/// vectors, respectively, and further optimizes them.
	/// * flags: Method for solving a PnP problem: see [calib3d_solvePnP_flags]
	/// * criteria: Termination criteria for internal undistortPoints call.
	/// The function interally undistorts points with [undistortPoints] and call [cv::solvePnP],
	/// thus the input are very similar. More information about Perspective-n-Points is described in [calib3d_solvePnP]
	/// for more information.
	///
	/// ## C++ default parameters
	/// * use_extrinsic_guess: false
	/// * flags: SOLVEPNP_ITERATIVE
	/// * criteria: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,10,1e-8)
	#[inline]
	pub fn solve_pnp_1(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvec: &mut impl ToOutputArray, tvec: &mut impl ToOutputArray, use_extrinsic_guess: bool, flags: i32, criteria: core::TermCriteria) -> Result<bool> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(rvec);
		output_array_arg!(tvec);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_solvePnP_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_bool_int_TermCriteria(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__OutputArray(), tvec.as_raw__OutputArray(), use_extrinsic_guess, flags, &criteria, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Performs stereo calibration
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of the calibration pattern points.
	/// * imagePoints1: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the first camera.
	/// * imagePoints2: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the second camera.
	/// * K1: Input/output first camera intrinsic matrix:
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cvecthreethree%7Bf%5Fx%5E%7B%28j%29%7D%7D%7B0%7D%7Bc%5Fx%5E%7B%28j%29%7D%7D%7B0%7D%7Bf%5Fy%5E%7B%28j%29%7D%7D%7Bc%5Fy%5E%7B%28j%29%7D%7D%7B0%7D%7B0%7D%7B1%7D) , ![inline formula](https://latex.codecogs.com/png.latex?j%20%3D%200%2C%5C%2C%201) . If
	/// any of [fisheye::CALIB_USE_INTRINSIC_GUESS] , [fisheye::CALIB_FIX_INTRINSIC] are specified,
	/// some or all of the matrix components must be initialized.
	/// * D1: Input/output vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye) of 4 elements.
	/// * K2: Input/output second camera intrinsic matrix. The parameter is similar to K1 .
	/// * D2: Input/output lens distortion coefficients for the second camera. The parameter is
	/// similar to D1 .
	/// * imageSize: Size of the image used only to initialize camera intrinsic matrix.
	/// * R: Output rotation matrix between the 1st and the 2nd camera coordinate systems.
	/// * T: Output translation vector between the coordinate systems of the cameras.
	/// * rvecs: Output vector of rotation vectors ( [Rodrigues] ) estimated for each pattern view in the
	/// coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
	/// i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
	/// description) brings the calibration pattern from the object coordinate space (in which object points are
	/// specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
	/// the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
	/// to camera coordinate space of the first camera of the stereo pair.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view, see parameter description
	/// of previous output parameter ( rvecs ).
	/// * flags: Different flags that may be zero or a combination of the following values:
	/// *    [fisheye::CALIB_FIX_INTRINSIC]  Fix K1, K2? and D1, D2? so that only R, T matrices
	/// are estimated.
	/// *    [fisheye::CALIB_USE_INTRINSIC_GUESS]  K1, K2 contains valid initial values of
	/// fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
	/// center (imageSize is used), and focal distances are computed in a least-squares fashion.
	/// *    [fisheye::CALIB_RECOMPUTE_EXTRINSIC]  Extrinsic will be recomputed after each iteration
	/// of intrinsic optimization.
	/// *    [fisheye::CALIB_CHECK_COND]  The functions will check validity of condition number.
	/// *    [fisheye::CALIB_FIX_SKEW]  Skew coefficient (alpha) is set to zero and stay zero.
	/// *   [fisheye::CALIB_FIX_K1],..., [fisheye::CALIB_FIX_K4] Selected distortion coefficients are set to zeros and stay
	/// zero.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// ## Overloaded parameters
	///
	///
	/// ## Note
	/// This alternative version of [fisheye_stereo_calibrate] function uses the following default values for its arguments:
	/// * flags: fisheye::CALIB_FIX_INTRINSIC
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,100,DBL_EPSILON)
	#[inline]
	pub fn fisheye_stereo_calibrate_def(object_points: &impl ToInputArray, image_points1: &impl ToInputArray, image_points2: &impl ToInputArray, k1: &mut impl ToInputOutputArray, d1: &mut impl ToInputOutputArray, k2: &mut impl ToInputOutputArray, d2: &mut impl ToInputOutputArray, image_size: core::Size, r: &mut impl ToOutputArray, t: &mut impl ToOutputArray) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points1);
		input_array_arg!(image_points2);
		input_output_array_arg!(k1);
		input_output_array_arg!(d1);
		input_output_array_arg!(k2);
		input_output_array_arg!(d2);
		output_array_arg!(r);
		output_array_arg!(t);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_stereoCalibrate_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_Size_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points1.as_raw__InputArray(), image_points2.as_raw__InputArray(), k1.as_raw__InputOutputArray(), d1.as_raw__InputOutputArray(), k2.as_raw__InputOutputArray(), d2.as_raw__InputOutputArray(), &image_size, r.as_raw__OutputArray(), t.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Performs stereo calibration
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of the calibration pattern points.
	/// * imagePoints1: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the first camera.
	/// * imagePoints2: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the second camera.
	/// * K1: Input/output first camera intrinsic matrix:
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cvecthreethree%7Bf%5Fx%5E%7B%28j%29%7D%7D%7B0%7D%7Bc%5Fx%5E%7B%28j%29%7D%7D%7B0%7D%7Bf%5Fy%5E%7B%28j%29%7D%7D%7Bc%5Fy%5E%7B%28j%29%7D%7D%7B0%7D%7B0%7D%7B1%7D) , ![inline formula](https://latex.codecogs.com/png.latex?j%20%3D%200%2C%5C%2C%201) . If
	/// any of [fisheye::CALIB_USE_INTRINSIC_GUESS] , [fisheye::CALIB_FIX_INTRINSIC] are specified,
	/// some or all of the matrix components must be initialized.
	/// * D1: Input/output vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye) of 4 elements.
	/// * K2: Input/output second camera intrinsic matrix. The parameter is similar to K1 .
	/// * D2: Input/output lens distortion coefficients for the second camera. The parameter is
	/// similar to D1 .
	/// * imageSize: Size of the image used only to initialize camera intrinsic matrix.
	/// * R: Output rotation matrix between the 1st and the 2nd camera coordinate systems.
	/// * T: Output translation vector between the coordinate systems of the cameras.
	/// * rvecs: Output vector of rotation vectors ( [Rodrigues] ) estimated for each pattern view in the
	/// coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
	/// i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
	/// description) brings the calibration pattern from the object coordinate space (in which object points are
	/// specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
	/// the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
	/// to camera coordinate space of the first camera of the stereo pair.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view, see parameter description
	/// of previous output parameter ( rvecs ).
	/// * flags: Different flags that may be zero or a combination of the following values:
	/// *    [fisheye::CALIB_FIX_INTRINSIC]  Fix K1, K2? and D1, D2? so that only R, T matrices
	/// are estimated.
	/// *    [fisheye::CALIB_USE_INTRINSIC_GUESS]  K1, K2 contains valid initial values of
	/// fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
	/// center (imageSize is used), and focal distances are computed in a least-squares fashion.
	/// *    [fisheye::CALIB_RECOMPUTE_EXTRINSIC]  Extrinsic will be recomputed after each iteration
	/// of intrinsic optimization.
	/// *    [fisheye::CALIB_CHECK_COND]  The functions will check validity of condition number.
	/// *    [fisheye::CALIB_FIX_SKEW]  Skew coefficient (alpha) is set to zero and stay zero.
	/// *   [fisheye::CALIB_FIX_K1],..., [fisheye::CALIB_FIX_K4] Selected distortion coefficients are set to zeros and stay
	/// zero.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// ## Note
	/// This alternative version of [stereo_calibrate_2] function uses the following default values for its arguments:
	/// * flags: fisheye::CALIB_FIX_INTRINSIC
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,100,DBL_EPSILON)
	#[inline]
	pub fn stereo_calibrate_2_def(object_points: &impl ToInputArray, image_points1: &impl ToInputArray, image_points2: &impl ToInputArray, k1: &mut impl ToInputOutputArray, d1: &mut impl ToInputOutputArray, k2: &mut impl ToInputOutputArray, d2: &mut impl ToInputOutputArray, image_size: core::Size, r: &mut impl ToOutputArray, t: &mut impl ToOutputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points1);
		input_array_arg!(image_points2);
		input_output_array_arg!(k1);
		input_output_array_arg!(d1);
		input_output_array_arg!(k2);
		input_output_array_arg!(d2);
		output_array_arg!(r);
		output_array_arg!(t);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_stereoCalibrate_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_Size_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points1.as_raw__InputArray(), image_points2.as_raw__InputArray(), k1.as_raw__InputOutputArray(), d1.as_raw__InputOutputArray(), k2.as_raw__InputOutputArray(), d2.as_raw__InputOutputArray(), &image_size, r.as_raw__OutputArray(), t.as_raw__OutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Performs stereo calibration
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of the calibration pattern points.
	/// * imagePoints1: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the first camera.
	/// * imagePoints2: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the second camera.
	/// * K1: Input/output first camera intrinsic matrix:
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cvecthreethree%7Bf%5Fx%5E%7B%28j%29%7D%7D%7B0%7D%7Bc%5Fx%5E%7B%28j%29%7D%7D%7B0%7D%7Bf%5Fy%5E%7B%28j%29%7D%7D%7Bc%5Fy%5E%7B%28j%29%7D%7D%7B0%7D%7B0%7D%7B1%7D) , ![inline formula](https://latex.codecogs.com/png.latex?j%20%3D%200%2C%5C%2C%201) . If
	/// any of [fisheye::CALIB_USE_INTRINSIC_GUESS] , [fisheye::CALIB_FIX_INTRINSIC] are specified,
	/// some or all of the matrix components must be initialized.
	/// * D1: Input/output vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye) of 4 elements.
	/// * K2: Input/output second camera intrinsic matrix. The parameter is similar to K1 .
	/// * D2: Input/output lens distortion coefficients for the second camera. The parameter is
	/// similar to D1 .
	/// * imageSize: Size of the image used only to initialize camera intrinsic matrix.
	/// * R: Output rotation matrix between the 1st and the 2nd camera coordinate systems.
	/// * T: Output translation vector between the coordinate systems of the cameras.
	/// * rvecs: Output vector of rotation vectors ( [Rodrigues] ) estimated for each pattern view in the
	/// coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
	/// i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
	/// description) brings the calibration pattern from the object coordinate space (in which object points are
	/// specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
	/// the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
	/// to camera coordinate space of the first camera of the stereo pair.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view, see parameter description
	/// of previous output parameter ( rvecs ).
	/// * flags: Different flags that may be zero or a combination of the following values:
	/// *    [fisheye::CALIB_FIX_INTRINSIC]  Fix K1, K2? and D1, D2? so that only R, T matrices
	/// are estimated.
	/// *    [fisheye::CALIB_USE_INTRINSIC_GUESS]  K1, K2 contains valid initial values of
	/// fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
	/// center (imageSize is used), and focal distances are computed in a least-squares fashion.
	/// *    [fisheye::CALIB_RECOMPUTE_EXTRINSIC]  Extrinsic will be recomputed after each iteration
	/// of intrinsic optimization.
	/// *    [fisheye::CALIB_CHECK_COND]  The functions will check validity of condition number.
	/// *    [fisheye::CALIB_FIX_SKEW]  Skew coefficient (alpha) is set to zero and stay zero.
	/// *   [fisheye::CALIB_FIX_K1],..., [fisheye::CALIB_FIX_K4] Selected distortion coefficients are set to zeros and stay
	/// zero.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// ## C++ default parameters
	/// * flags: fisheye::CALIB_FIX_INTRINSIC
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,100,DBL_EPSILON)
	#[inline]
	pub fn stereo_calibrate_2(object_points: &impl ToInputArray, image_points1: &impl ToInputArray, image_points2: &impl ToInputArray, k1: &mut impl ToInputOutputArray, d1: &mut impl ToInputOutputArray, k2: &mut impl ToInputOutputArray, d2: &mut impl ToInputOutputArray, image_size: core::Size, r: &mut impl ToOutputArray, t: &mut impl ToOutputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray, flags: i32, criteria: core::TermCriteria) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points1);
		input_array_arg!(image_points2);
		input_output_array_arg!(k1);
		input_output_array_arg!(d1);
		input_output_array_arg!(k2);
		input_output_array_arg!(d2);
		output_array_arg!(r);
		output_array_arg!(t);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_stereoCalibrate_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_Size_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_TermCriteria(object_points.as_raw__InputArray(), image_points1.as_raw__InputArray(), image_points2.as_raw__InputArray(), k1.as_raw__InputOutputArray(), d1.as_raw__InputOutputArray(), k2.as_raw__InputOutputArray(), d2.as_raw__InputOutputArray(), &image_size, r.as_raw__OutputArray(), t.as_raw__OutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), flags, &criteria, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Performs stereo calibration
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of the calibration pattern points.
	/// * imagePoints1: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the first camera.
	/// * imagePoints2: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the second camera.
	/// * K1: Input/output first camera intrinsic matrix:
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cvecthreethree%7Bf%5Fx%5E%7B%28j%29%7D%7D%7B0%7D%7Bc%5Fx%5E%7B%28j%29%7D%7D%7B0%7D%7Bf%5Fy%5E%7B%28j%29%7D%7D%7Bc%5Fy%5E%7B%28j%29%7D%7D%7B0%7D%7B0%7D%7B1%7D) , ![inline formula](https://latex.codecogs.com/png.latex?j%20%3D%200%2C%5C%2C%201) . If
	/// any of [fisheye::CALIB_USE_INTRINSIC_GUESS] , [fisheye::CALIB_FIX_INTRINSIC] are specified,
	/// some or all of the matrix components must be initialized.
	/// * D1: Input/output vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye) of 4 elements.
	/// * K2: Input/output second camera intrinsic matrix. The parameter is similar to K1 .
	/// * D2: Input/output lens distortion coefficients for the second camera. The parameter is
	/// similar to D1 .
	/// * imageSize: Size of the image used only to initialize camera intrinsic matrix.
	/// * R: Output rotation matrix between the 1st and the 2nd camera coordinate systems.
	/// * T: Output translation vector between the coordinate systems of the cameras.
	/// * rvecs: Output vector of rotation vectors ( [Rodrigues] ) estimated for each pattern view in the
	/// coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
	/// i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
	/// description) brings the calibration pattern from the object coordinate space (in which object points are
	/// specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
	/// the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
	/// to camera coordinate space of the first camera of the stereo pair.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view, see parameter description
	/// of previous output parameter ( rvecs ).
	/// * flags: Different flags that may be zero or a combination of the following values:
	/// *    [fisheye::CALIB_FIX_INTRINSIC]  Fix K1, K2? and D1, D2? so that only R, T matrices
	/// are estimated.
	/// *    [fisheye::CALIB_USE_INTRINSIC_GUESS]  K1, K2 contains valid initial values of
	/// fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
	/// center (imageSize is used), and focal distances are computed in a least-squares fashion.
	/// *    [fisheye::CALIB_RECOMPUTE_EXTRINSIC]  Extrinsic will be recomputed after each iteration
	/// of intrinsic optimization.
	/// *    [fisheye::CALIB_CHECK_COND]  The functions will check validity of condition number.
	/// *    [fisheye::CALIB_FIX_SKEW]  Skew coefficient (alpha) is set to zero and stay zero.
	/// *   [fisheye::CALIB_FIX_K1],..., [fisheye::CALIB_FIX_K4] Selected distortion coefficients are set to zeros and stay
	/// zero.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// ## Overloaded parameters
	///
	/// ## C++ default parameters
	/// * flags: fisheye::CALIB_FIX_INTRINSIC
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,100,DBL_EPSILON)
	#[inline]
	pub fn fisheye_stereo_calibrate(object_points: &impl ToInputArray, image_points1: &impl ToInputArray, image_points2: &impl ToInputArray, k1: &mut impl ToInputOutputArray, d1: &mut impl ToInputOutputArray, k2: &mut impl ToInputOutputArray, d2: &mut impl ToInputOutputArray, image_size: core::Size, r: &mut impl ToOutputArray, t: &mut impl ToOutputArray, flags: i32, criteria: core::TermCriteria) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points1);
		input_array_arg!(image_points2);
		input_output_array_arg!(k1);
		input_output_array_arg!(d1);
		input_output_array_arg!(k2);
		input_output_array_arg!(d2);
		output_array_arg!(r);
		output_array_arg!(t);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_stereoCalibrate_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_Size_const__OutputArrayR_const__OutputArrayR_int_TermCriteria(object_points.as_raw__InputArray(), image_points1.as_raw__InputArray(), image_points2.as_raw__InputArray(), k1.as_raw__InputOutputArray(), d1.as_raw__InputOutputArray(), k2.as_raw__InputOutputArray(), d2.as_raw__InputOutputArray(), &image_size, r.as_raw__OutputArray(), t.as_raw__OutputArray(), flags, &criteria, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Stereo rectification for fisheye camera model
	///
	/// ## Parameters
	/// * K1: First camera intrinsic matrix.
	/// * D1: First camera distortion parameters.
	/// * K2: Second camera intrinsic matrix.
	/// * D2: Second camera distortion parameters.
	/// * imageSize: Size of the image used for stereo calibration.
	/// * R: Rotation matrix between the coordinate systems of the first and the second
	/// cameras.
	/// * tvec: Translation vector between coordinate systems of the cameras.
	/// * R1: Output 3x3 rectification transform (rotation matrix) for the first camera.
	/// * R2: Output 3x3 rectification transform (rotation matrix) for the second camera.
	/// * P1: Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
	/// camera.
	/// * P2: Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
	/// camera.
	/// * Q: Output ![inline formula](https://latex.codecogs.com/png.latex?4%20%5Ctimes%204) disparity-to-depth mapping matrix (see [reproject_image_to_3d] ).
	/// * flags: Operation flags that may be zero or [fisheye::CALIB_ZERO_DISPARITY] . If the flag is set,
	/// the function makes the principal points of each camera have the same pixel coordinates in the
	/// rectified views. And if the flag is not set, the function may still shift the images in the
	/// horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
	/// useful image area.
	/// * newImageSize: New image resolution after rectification. The same size should be passed to
	/// [init_undistort_rectify_map] (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
	/// is passed (default), it is set to the original imageSize . Setting it to larger value can help you
	/// preserve details in the original image, especially when there is a big radial distortion.
	/// * balance: Sets the new focal length in range between the min focal length and the max focal
	/// length. Balance is in range of [0, 1].
	/// * fov_scale: Divisor for new focal length.
	///
	/// ## Note
	/// This alternative version of [fisheye_stereo_rectify] function uses the following default values for its arguments:
	/// * new_image_size: Size()
	/// * balance: 0.0
	/// * fov_scale: 1.0
	#[inline]
	pub fn fisheye_stereo_rectify_def(k1: &impl ToInputArray, d1: &impl ToInputArray, k2: &impl ToInputArray, d2: &impl ToInputArray, image_size: core::Size, r: &impl ToInputArray, tvec: &impl ToInputArray, r1: &mut impl ToOutputArray, r2: &mut impl ToOutputArray, p1: &mut impl ToOutputArray, p2: &mut impl ToOutputArray, q: &mut impl ToOutputArray, flags: i32) -> Result<()> {
		input_array_arg!(k1);
		input_array_arg!(d1);
		input_array_arg!(k2);
		input_array_arg!(d2);
		input_array_arg!(r);
		input_array_arg!(tvec);
		output_array_arg!(r1);
		output_array_arg!(r2);
		output_array_arg!(p1);
		output_array_arg!(p2);
		output_array_arg!(q);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_stereoRectify_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const_SizeR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int(k1.as_raw__InputArray(), d1.as_raw__InputArray(), k2.as_raw__InputArray(), d2.as_raw__InputArray(), &image_size, r.as_raw__InputArray(), tvec.as_raw__InputArray(), r1.as_raw__OutputArray(), r2.as_raw__OutputArray(), p1.as_raw__OutputArray(), p2.as_raw__OutputArray(), q.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Stereo rectification for fisheye camera model
	///
	/// ## Parameters
	/// * K1: First camera intrinsic matrix.
	/// * D1: First camera distortion parameters.
	/// * K2: Second camera intrinsic matrix.
	/// * D2: Second camera distortion parameters.
	/// * imageSize: Size of the image used for stereo calibration.
	/// * R: Rotation matrix between the coordinate systems of the first and the second
	/// cameras.
	/// * tvec: Translation vector between coordinate systems of the cameras.
	/// * R1: Output 3x3 rectification transform (rotation matrix) for the first camera.
	/// * R2: Output 3x3 rectification transform (rotation matrix) for the second camera.
	/// * P1: Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
	/// camera.
	/// * P2: Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
	/// camera.
	/// * Q: Output ![inline formula](https://latex.codecogs.com/png.latex?4%20%5Ctimes%204) disparity-to-depth mapping matrix (see [reproject_image_to_3d] ).
	/// * flags: Operation flags that may be zero or [fisheye::CALIB_ZERO_DISPARITY] . If the flag is set,
	/// the function makes the principal points of each camera have the same pixel coordinates in the
	/// rectified views. And if the flag is not set, the function may still shift the images in the
	/// horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
	/// useful image area.
	/// * newImageSize: New image resolution after rectification. The same size should be passed to
	/// [init_undistort_rectify_map] (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
	/// is passed (default), it is set to the original imageSize . Setting it to larger value can help you
	/// preserve details in the original image, especially when there is a big radial distortion.
	/// * balance: Sets the new focal length in range between the min focal length and the max focal
	/// length. Balance is in range of [0, 1].
	/// * fov_scale: Divisor for new focal length.
	///
	/// ## C++ default parameters
	/// * new_image_size: Size()
	/// * balance: 0.0
	/// * fov_scale: 1.0
	#[inline]
	pub fn fisheye_stereo_rectify(k1: &impl ToInputArray, d1: &impl ToInputArray, k2: &impl ToInputArray, d2: &impl ToInputArray, image_size: core::Size, r: &impl ToInputArray, tvec: &impl ToInputArray, r1: &mut impl ToOutputArray, r2: &mut impl ToOutputArray, p1: &mut impl ToOutputArray, p2: &mut impl ToOutputArray, q: &mut impl ToOutputArray, flags: i32, new_image_size: core::Size, balance: f64, fov_scale: f64) -> Result<()> {
		input_array_arg!(k1);
		input_array_arg!(d1);
		input_array_arg!(k2);
		input_array_arg!(d2);
		input_array_arg!(r);
		input_array_arg!(tvec);
		output_array_arg!(r1);
		output_array_arg!(r2);
		output_array_arg!(p1);
		output_array_arg!(p2);
		output_array_arg!(q);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_stereoRectify_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const_SizeR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_const_SizeR_double_double(k1.as_raw__InputArray(), d1.as_raw__InputArray(), k2.as_raw__InputArray(), d2.as_raw__InputArray(), &image_size, r.as_raw__InputArray(), tvec.as_raw__InputArray(), r1.as_raw__OutputArray(), r2.as_raw__OutputArray(), p1.as_raw__OutputArray(), p2.as_raw__OutputArray(), q.as_raw__OutputArray(), flags, &new_image_size, balance, fov_scale, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Transforms an image to compensate for fisheye lens distortion.
	///
	/// ## Parameters
	/// * distorted: image with fisheye lens distortion.
	/// * undistorted: Output image with compensated fisheye lens distortion.
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * Knew: Camera intrinsic matrix of the distorted image. By default, it is the identity matrix but you
	/// may additionally scale and shift the result by using a different matrix.
	/// * new_size: the new size
	///
	/// The function transforms an image to compensate radial and tangential lens distortion.
	///
	/// The function is simply a combination of #fisheye::initUndistortRectifyMap (with unity R ) and [remap]
	/// (with bilinear interpolation). See the former function for details of the transformation being
	/// performed.
	///
	/// See below the results of undistortImage.
	///    *   a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3,
	///        k_4, k_5, k_6) of distortion were optimized under calibration)
	///    *   b\) result of #fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2,
	///        k_3, k_4) of fisheye distortion were optimized under calibration)
	///    *   c\) original image was captured with fisheye lens
	///
	/// Pictures a) and b) almost the same. But if we consider points of image located far from the center
	/// of image, we can notice that on image a) these points are distorted.
	///
	/// ![image](https://docs.opencv.org/4.12.0/fisheye_undistorted.jpg)
	///
	/// ## Note
	/// This alternative version of [fisheye_undistort_image] function uses the following default values for its arguments:
	/// * knew: cv::noArray()
	/// * new_size: Size()
	#[inline]
	pub fn fisheye_undistort_image_def(distorted: &impl ToInputArray, undistorted: &mut impl ToOutputArray, k: &impl ToInputArray, d: &impl ToInputArray) -> Result<()> {
		input_array_arg!(distorted);
		output_array_arg!(undistorted);
		input_array_arg!(k);
		input_array_arg!(d);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_undistortImage_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR(distorted.as_raw__InputArray(), undistorted.as_raw__OutputArray(), k.as_raw__InputArray(), d.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Transforms an image to compensate for fisheye lens distortion.
	///
	/// ## Parameters
	/// * distorted: image with fisheye lens distortion.
	/// * undistorted: Output image with compensated fisheye lens distortion.
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * Knew: Camera intrinsic matrix of the distorted image. By default, it is the identity matrix but you
	/// may additionally scale and shift the result by using a different matrix.
	/// * new_size: the new size
	///
	/// The function transforms an image to compensate radial and tangential lens distortion.
	///
	/// The function is simply a combination of #fisheye::initUndistortRectifyMap (with unity R ) and [remap]
	/// (with bilinear interpolation). See the former function for details of the transformation being
	/// performed.
	///
	/// See below the results of undistortImage.
	///    *   a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3,
	///        k_4, k_5, k_6) of distortion were optimized under calibration)
	///    *   b\) result of #fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2,
	///        k_3, k_4) of fisheye distortion were optimized under calibration)
	///    *   c\) original image was captured with fisheye lens
	///
	/// Pictures a) and b) almost the same. But if we consider points of image located far from the center
	/// of image, we can notice that on image a) these points are distorted.
	///
	/// ![image](https://docs.opencv.org/4.12.0/fisheye_undistorted.jpg)
	///
	/// ## C++ default parameters
	/// * knew: cv::noArray()
	/// * new_size: Size()
	#[inline]
	pub fn fisheye_undistort_image(distorted: &impl ToInputArray, undistorted: &mut impl ToOutputArray, k: &impl ToInputArray, d: &impl ToInputArray, knew: &impl ToInputArray, new_size: core::Size) -> Result<()> {
		input_array_arg!(distorted);
		output_array_arg!(undistorted);
		input_array_arg!(k);
		input_array_arg!(d);
		input_array_arg!(knew);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_undistortImage_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const_SizeR(distorted.as_raw__InputArray(), undistorted.as_raw__OutputArray(), k.as_raw__InputArray(), d.as_raw__InputArray(), knew.as_raw__InputArray(), &new_size, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Undistorts 2D points using fisheye model
	///
	/// ## Parameters
	/// * distorted: Array of object points, 1xN/Nx1 2-channel (or vector\<Point2f\> ), where N is the
	/// number of points in the view.
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * R: Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3
	/// 1-channel or 1x1 3-channel
	/// * P: New camera intrinsic matrix (3x3) or new projection matrix (3x4)
	/// * criteria: Termination criteria
	/// * undistorted: Output array of image points, 1xN/Nx1 2-channel, or vector\<Point2f\> .
	///
	/// ## Note
	/// This alternative version of [fisheye_undistort_points] function uses the following default values for its arguments:
	/// * r: noArray()
	/// * p: noArray()
	/// * criteria: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,10,1e-8)
	#[inline]
	pub fn fisheye_undistort_points_def(distorted: &impl ToInputArray, undistorted: &mut impl ToOutputArray, k: &impl ToInputArray, d: &impl ToInputArray) -> Result<()> {
		input_array_arg!(distorted);
		output_array_arg!(undistorted);
		input_array_arg!(k);
		input_array_arg!(d);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_undistortPoints_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR(distorted.as_raw__InputArray(), undistorted.as_raw__OutputArray(), k.as_raw__InputArray(), d.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Undistorts 2D points using fisheye model
	///
	/// ## Parameters
	/// * distorted: Array of object points, 1xN/Nx1 2-channel (or vector\<Point2f\> ), where N is the
	/// number of points in the view.
	/// * K: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BK%7D).
	/// * D: Input vector of distortion coefficients ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffsfisheye).
	/// * R: Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3
	/// 1-channel or 1x1 3-channel
	/// * P: New camera intrinsic matrix (3x3) or new projection matrix (3x4)
	/// * criteria: Termination criteria
	/// * undistorted: Output array of image points, 1xN/Nx1 2-channel, or vector\<Point2f\> .
	///
	/// ## C++ default parameters
	/// * r: noArray()
	/// * p: noArray()
	/// * criteria: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,10,1e-8)
	#[inline]
	pub fn fisheye_undistort_points(distorted: &impl ToInputArray, undistorted: &mut impl ToOutputArray, k: &impl ToInputArray, d: &impl ToInputArray, r: &impl ToInputArray, p: &impl ToInputArray, criteria: core::TermCriteria) -> Result<()> {
		input_array_arg!(distorted);
		output_array_arg!(undistorted);
		input_array_arg!(k);
		input_array_arg!(d);
		input_array_arg!(r);
		input_array_arg!(p);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_fisheye_undistortPoints_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_TermCriteria(distorted.as_raw__InputArray(), undistorted.as_raw__OutputArray(), k.as_raw__InputArray(), d.as_raw__InputArray(), r.as_raw__InputArray(), p.as_raw__InputArray(), &criteria, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Returns the default new camera matrix.
	///
	/// The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when
	/// centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).
	///
	/// In the latter case, the new camera matrix will be:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%26%200%20%26%26%20%28%20%5Ctexttt%7BimgSize%2Ewidth%7D%20%2D1%29%2A0%2E5%20%20%5C%5C%200%20%26%26%20f%5Fy%20%26%26%20%28%20%5Ctexttt%7BimgSize%2Eheight%7D%20%2D1%29%2A0%2E5%20%20%5C%5C%200%20%26%26%200%20%26%26%201%20%5Cend%7Bbmatrix%7D%20%2C)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5Fy) are ![inline formula](https://latex.codecogs.com/png.latex?%280%2C0%29) and ![inline formula](https://latex.codecogs.com/png.latex?%281%2C1%29) elements of cameraMatrix, respectively.
	///
	/// By default, the undistortion functions in OpenCV (see #initUndistortRectifyMap, #undistort) do not
	/// move the principal point. However, when you work with stereo, it is important to move the principal
	/// points in both views to the same y-coordinate (which is required by most of stereo correspondence
	/// algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for
	/// each view where the principal points are located at the center.
	///
	/// ## Parameters
	/// * cameraMatrix: Input camera matrix.
	/// * imgsize: Camera view image size in pixels.
	/// * centerPrincipalPoint: Location of the principal point in the new camera matrix. The
	/// parameter indicates whether this location should be at the image center or not.
	///
	/// ## Note
	/// This alternative version of [get_default_new_camera_matrix] function uses the following default values for its arguments:
	/// * imgsize: Size()
	/// * center_principal_point: false
	#[inline]
	pub fn get_default_new_camera_matrix_def(camera_matrix: &impl ToInputArray) -> Result<core::Mat> {
		input_array_arg!(camera_matrix);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_getDefaultNewCameraMatrix_const__InputArrayR(camera_matrix.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Returns the default new camera matrix.
	///
	/// The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when
	/// centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).
	///
	/// In the latter case, the new camera matrix will be:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%26%200%20%26%26%20%28%20%5Ctexttt%7BimgSize%2Ewidth%7D%20%2D1%29%2A0%2E5%20%20%5C%5C%200%20%26%26%20f%5Fy%20%26%26%20%28%20%5Ctexttt%7BimgSize%2Eheight%7D%20%2D1%29%2A0%2E5%20%20%5C%5C%200%20%26%26%200%20%26%26%201%20%5Cend%7Bbmatrix%7D%20%2C)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5Fy) are ![inline formula](https://latex.codecogs.com/png.latex?%280%2C0%29) and ![inline formula](https://latex.codecogs.com/png.latex?%281%2C1%29) elements of cameraMatrix, respectively.
	///
	/// By default, the undistortion functions in OpenCV (see #initUndistortRectifyMap, #undistort) do not
	/// move the principal point. However, when you work with stereo, it is important to move the principal
	/// points in both views to the same y-coordinate (which is required by most of stereo correspondence
	/// algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for
	/// each view where the principal points are located at the center.
	///
	/// ## Parameters
	/// * cameraMatrix: Input camera matrix.
	/// * imgsize: Camera view image size in pixels.
	/// * centerPrincipalPoint: Location of the principal point in the new camera matrix. The
	/// parameter indicates whether this location should be at the image center or not.
	///
	/// ## C++ default parameters
	/// * imgsize: Size()
	/// * center_principal_point: false
	#[inline]
	pub fn get_default_new_camera_matrix(camera_matrix: &impl ToInputArray, imgsize: core::Size, center_principal_point: bool) -> Result<core::Mat> {
		input_array_arg!(camera_matrix);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_getDefaultNewCameraMatrix_const__InputArrayR_Size_bool(camera_matrix.as_raw__InputArray(), &imgsize, center_principal_point, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Returns the new camera intrinsic matrix based on the free scaling parameter.
	///
	/// ## Parameters
	/// * cameraMatrix: Input camera intrinsic matrix.
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs). If the vector is NULL/empty, the zero distortion coefficients are
	/// assumed.
	/// * imageSize: Original image size.
	/// * alpha: Free scaling parameter between 0 (when all the pixels in the undistorted image are
	/// valid) and 1 (when all the source image pixels are retained in the undistorted image). See
	/// [stereo_rectify] for details.
	/// * newImgSize: Image size after rectification. By default, it is set to imageSize .
	/// * validPixROI: Optional output rectangle that outlines all-good-pixels region in the
	/// undistorted image. See roi1, roi2 description in [stereo_rectify] .
	/// * centerPrincipalPoint: Optional flag that indicates whether in the new camera intrinsic matrix the
	/// principal point should be at the image center or not. By default, the principal point is chosen to
	/// best fit a subset of the source image (determined by alpha) to the corrected image.
	/// ## Returns
	/// new_camera_matrix Output new camera intrinsic matrix.
	///
	/// The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter.
	/// By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original
	/// image pixels if there is valuable information in the corners alpha=1 , or get something in between.
	/// When alpha\>0 , the undistorted result is likely to have some black pixels corresponding to
	/// "virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion
	/// coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to
	/// [init_undistort_rectify_map] to produce the maps for [remap] .
	///
	/// ## Note
	/// This alternative version of [get_optimal_new_camera_matrix] function uses the following default values for its arguments:
	/// * new_img_size: Size()
	/// * valid_pix_roi: 0
	/// * center_principal_point: false
	#[inline]
	pub fn get_optimal_new_camera_matrix_def(camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, image_size: core::Size, alpha: f64) -> Result<core::Mat> {
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_getOptimalNewCameraMatrix_const__InputArrayR_const__InputArrayR_Size_double(camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), &image_size, alpha, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Returns the new camera intrinsic matrix based on the free scaling parameter.
	///
	/// ## Parameters
	/// * cameraMatrix: Input camera intrinsic matrix.
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs). If the vector is NULL/empty, the zero distortion coefficients are
	/// assumed.
	/// * imageSize: Original image size.
	/// * alpha: Free scaling parameter between 0 (when all the pixels in the undistorted image are
	/// valid) and 1 (when all the source image pixels are retained in the undistorted image). See
	/// [stereo_rectify] for details.
	/// * newImgSize: Image size after rectification. By default, it is set to imageSize .
	/// * validPixROI: Optional output rectangle that outlines all-good-pixels region in the
	/// undistorted image. See roi1, roi2 description in [stereo_rectify] .
	/// * centerPrincipalPoint: Optional flag that indicates whether in the new camera intrinsic matrix the
	/// principal point should be at the image center or not. By default, the principal point is chosen to
	/// best fit a subset of the source image (determined by alpha) to the corrected image.
	/// ## Returns
	/// new_camera_matrix Output new camera intrinsic matrix.
	///
	/// The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter.
	/// By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original
	/// image pixels if there is valuable information in the corners alpha=1 , or get something in between.
	/// When alpha\>0 , the undistorted result is likely to have some black pixels corresponding to
	/// "virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion
	/// coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to
	/// [init_undistort_rectify_map] to produce the maps for [remap] .
	///
	/// ## C++ default parameters
	/// * new_img_size: Size()
	/// * valid_pix_roi: 0
	/// * center_principal_point: false
	#[inline]
	pub fn get_optimal_new_camera_matrix(camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, image_size: core::Size, alpha: f64, new_img_size: core::Size, valid_pix_roi: Option<&mut core::Rect>, center_principal_point: bool) -> Result<core::Mat> {
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_getOptimalNewCameraMatrix_const__InputArrayR_const__InputArrayR_Size_double_Size_RectX_bool(camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), &image_size, alpha, &new_img_size, valid_pix_roi.map_or(::core::ptr::null_mut(), |valid_pix_roi| valid_pix_roi), center_principal_point, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// computes valid disparity ROI from the valid ROIs of the rectified images (that are returned by #stereoRectify)
	#[inline]
	pub fn get_valid_disparity_roi(roi1: core::Rect, roi2: core::Rect, min_disparity: i32, number_of_disparities: i32, block_size: i32) -> Result<core::Rect> {
		return_send!(via ocvrs_return);
		unsafe { sys::cv_getValidDisparityROI_Rect_Rect_int_int_int(&roi1, &roi2, min_disparity, number_of_disparities, block_size, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds an initial camera intrinsic matrix from 3D-2D point correspondences.
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of the calibration pattern points in the calibration pattern
	/// coordinate space. In the old interface all the per-view vectors are concatenated. See
	/// [calibrate_camera] for details.
	/// * imagePoints: Vector of vectors of the projections of the calibration pattern points. In the
	/// old interface all the per-view vectors are concatenated.
	/// * imageSize: Image size in pixels used to initialize the principal point.
	/// * aspectRatio: If it is zero or negative, both ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5Fy) are estimated independently.
	/// Otherwise, ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx%20%3D%20f%5Fy%20%5Ccdot%20%5Ctexttt%7BaspectRatio%7D) .
	///
	/// The function estimates and returns an initial camera intrinsic matrix for the camera calibration process.
	/// Currently, the function only supports planar calibration patterns, which are patterns where each
	/// object point has z-coordinate =0.
	///
	/// ## Note
	/// This alternative version of [init_camera_matrix_2d] function uses the following default values for its arguments:
	/// * aspect_ratio: 1.0
	#[inline]
	pub fn init_camera_matrix_2d_def(object_points: &impl ToInputArray, image_points: &impl ToInputArray, image_size: core::Size) -> Result<core::Mat> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_initCameraMatrix2D_const__InputArrayR_const__InputArrayR_Size(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), &image_size, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Finds an initial camera intrinsic matrix from 3D-2D point correspondences.
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of the calibration pattern points in the calibration pattern
	/// coordinate space. In the old interface all the per-view vectors are concatenated. See
	/// [calibrate_camera] for details.
	/// * imagePoints: Vector of vectors of the projections of the calibration pattern points. In the
	/// old interface all the per-view vectors are concatenated.
	/// * imageSize: Image size in pixels used to initialize the principal point.
	/// * aspectRatio: If it is zero or negative, both ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5Fy) are estimated independently.
	/// Otherwise, ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx%20%3D%20f%5Fy%20%5Ccdot%20%5Ctexttt%7BaspectRatio%7D) .
	///
	/// The function estimates and returns an initial camera intrinsic matrix for the camera calibration process.
	/// Currently, the function only supports planar calibration patterns, which are patterns where each
	/// object point has z-coordinate =0.
	///
	/// ## C++ default parameters
	/// * aspect_ratio: 1.0
	#[inline]
	pub fn init_camera_matrix_2d(object_points: &impl ToInputArray, image_points: &impl ToInputArray, image_size: core::Size, aspect_ratio: f64) -> Result<core::Mat> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_initCameraMatrix2D_const__InputArrayR_const__InputArrayR_Size_double(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), &image_size, aspect_ratio, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		let ret = unsafe { core::Mat::opencv_from_extern(ret) };
		Ok(ret)
	}

	/// Computes the projection and inverse-rectification transformation map. In essense, this is the inverse of
	/// [init_undistort_rectify_map] to accomodate stereo-rectification of projectors ('inverse-cameras') in projector-camera pairs.
	///
	/// The function computes the joint projection and inverse rectification transformation and represents the
	/// result in the form of maps for #remap. The projected image looks like a distorted version of the original which,
	/// once projected by a projector, should visually match the original. In case of a monocular camera, newCameraMatrix
	/// is usually equal to cameraMatrix, or it can be computed by
	/// [get_optimal_new_camera_matrix] for a better control over scaling. In case of a projector-camera pair,
	/// newCameraMatrix is normally set to P1 or P2 computed by [stereo_rectify] .
	///
	/// The projector is oriented differently in the coordinate space, according to R. In case of projector-camera pairs,
	/// this helps align the projector (in the same manner as [init_undistort_rectify_map] for the camera) to create a stereo-rectified pair. This
	/// allows epipolar lines on both images to become horizontal and have the same y-coordinate (in case of a horizontally aligned projector-camera pair).
	///
	/// The function builds the maps for the inverse mapping algorithm that is used by #remap. That
	/// is, for each pixel ![inline formula](https://latex.codecogs.com/png.latex?%28u%2C%20v%29) in the destination (projected and inverse-rectified) image, the function
	/// computes the corresponding coordinates in the source image (that is, in the original digital image). The following process is applied:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Barray%7D%7Bl%7D%0A%5Ctext%7BnewCameraMatrix%7D%5C%5C%0Ax%20%20%5Cleftarrow%20%28u%20%2D%20%7Bc%27%7D%5Fx%29%2F%7Bf%27%7D%5Fx%20%20%5C%5C%0Ay%20%20%5Cleftarrow%20%28v%20%2D%20%7Bc%27%7D%5Fy%29%2F%7Bf%27%7D%5Fy%20%20%5C%5C%0A%0A%5C%5C%5Ctext%7BUndistortion%7D%0A%5C%5C%5Cscriptsize%7B%5Ctextit%7Bthough%20equation%20shown%20is%20for%20radial%20undistortion%2C%20function%20implements%20cv%3A%3AundistortPoints%28%29%7D%7D%5C%5C%0Ar%5E2%20%20%5Cleftarrow%20x%5E2%20%2B%20y%5E2%20%5C%5C%0A%5Ctheta%20%5Cleftarrow%20%5Cfrac%7B1%20%2B%20k%5F1%20r%5E2%20%2B%20k%5F2%20r%5E4%20%2B%20k%5F3%20r%5E6%7D%7B1%20%2B%20k%5F4%20r%5E2%20%2B%20k%5F5%20r%5E4%20%2B%20k%5F6%20r%5E6%7D%5C%5C%0Ax%27%20%5Cleftarrow%20%5Cfrac%7Bx%7D%7B%5Ctheta%7D%20%5C%5C%0Ay%27%20%20%5Cleftarrow%20%5Cfrac%7By%7D%7B%5Ctheta%7D%20%5C%5C%0A%0A%5C%5C%5Ctext%7BRectification%7D%5C%5C%0A%7B%5BX%5C%2CY%5C%2CW%5D%7D%20%5ET%20%20%5Cleftarrow%20R%2A%5Bx%27%20%5C%2C%20y%27%20%5C%2C%201%5D%5ET%20%20%5C%5C%0Ax%27%27%20%20%5Cleftarrow%20X%2FW%20%20%5C%5C%0Ay%27%27%20%20%5Cleftarrow%20Y%2FW%20%20%5C%5C%0A%0A%5C%5C%5Ctext%7BcameraMatrix%7D%5C%5C%0Amap%5Fx%28u%2Cv%29%20%20%5Cleftarrow%20x%27%27%20f%5Fx%20%2B%20c%5Fx%20%20%5C%5C%0Amap%5Fy%28u%2Cv%29%20%20%5Cleftarrow%20y%27%27%20f%5Fy%20%2B%20c%5Fy%0A%5Cend%7Barray%7D%0A)
	/// where ![inline formula](https://latex.codecogs.com/png.latex?%28k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%5B%2C%20k%5F3%5B%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%5B%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%20s%5F4%5B%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%5D%5D%5D%5D%29)
	/// are the distortion coefficients vector distCoeffs.
	///
	/// In case of a stereo-rectified projector-camera pair, this function is called for the projector while [init_undistort_rectify_map] is called for the camera head.
	/// This is done after #stereoRectify, which in turn is called after #stereoCalibrate. If the projector-camera pair
	/// is not calibrated, it is still possible to compute the rectification transformations directly from
	/// the fundamental matrix using #stereoRectifyUncalibrated. For the projector and camera, the function computes
	/// homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D
	/// space. R can be computed from H as
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BR%7D%20%3D%20%5Ctexttt%7BcameraMatrix%7D%20%5E%7B%2D1%7D%20%5Ccdot%20%5Ctexttt%7BH%7D%20%5Ccdot%20%5Ctexttt%7BcameraMatrix%7D)
	/// where cameraMatrix can be chosen arbitrarily.
	///
	/// ## Parameters
	/// * cameraMatrix: Input camera matrix ![inline formula](https://latex.codecogs.com/png.latex?A%3D%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%28k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%5B%2C%20k%5F3%5B%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%5B%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%20s%5F4%5B%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%5D%5D%5D%5D%29)
	/// of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
	/// * R: Optional rectification transformation in the object space (3x3 matrix). R1 or R2,
	/// computed by [stereo_rectify] can be passed here. If the matrix is empty, the identity transformation
	/// is assumed.
	/// * newCameraMatrix: New camera matrix ![inline formula](https://latex.codecogs.com/png.latex?A%27%3D%5Cbegin%7Bbmatrix%7D%20f%5Fx%27%20%26%200%20%26%20c%5Fx%27%5C%5C%200%20%26%20f%5Fy%27%20%26%20c%5Fy%27%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D).
	/// * size: Distorted image size.
	/// * m1type: Type of the first output map. Can be CV_32FC1, CV_32FC2 or CV_16SC2, see [convert_maps]
	/// * map1: The first output map for #remap.
	/// * map2: The second output map for #remap.
	#[inline]
	pub fn init_inverse_rectification_map(camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, r: &impl ToInputArray, new_camera_matrix: &impl ToInputArray, size: core::Size, m1type: i32, map1: &mut impl ToOutputArray, map2: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		input_array_arg!(r);
		input_array_arg!(new_camera_matrix);
		output_array_arg!(map1);
		output_array_arg!(map2);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_initInverseRectificationMap_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const_SizeR_int_const__OutputArrayR_const__OutputArrayR(camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), r.as_raw__InputArray(), new_camera_matrix.as_raw__InputArray(), &size, m1type, map1.as_raw__OutputArray(), map2.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes the undistortion and rectification transformation map.
	///
	/// The function computes the joint undistortion and rectification transformation and represents the
	/// result in the form of maps for #remap. The undistorted image looks like original, as if it is
	/// captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a
	/// monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by
	/// [get_optimal_new_camera_matrix] for a better control over scaling. In case of a stereo camera,
	/// newCameraMatrix is normally set to P1 or P2 computed by [stereo_rectify] .
	///
	/// Also, this new camera is oriented differently in the coordinate space, according to R. That, for
	/// example, helps to align two heads of a stereo camera so that the epipolar lines on both images
	/// become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).
	///
	/// The function actually builds the maps for the inverse mapping algorithm that is used by #remap. That
	/// is, for each pixel ![inline formula](https://latex.codecogs.com/png.latex?%28u%2C%20v%29) in the destination (corrected and rectified) image, the function
	/// computes the corresponding coordinates in the source image (that is, in the original image from
	/// camera). The following process is applied:
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Barray%7D%7Bl%7D%0Ax%20%20%5Cleftarrow%20%28u%20%2D%20%7Bc%27%7D%5Fx%29%2F%7Bf%27%7D%5Fx%20%20%5C%5C%0Ay%20%20%5Cleftarrow%20%28v%20%2D%20%7Bc%27%7D%5Fy%29%2F%7Bf%27%7D%5Fy%20%20%5C%5C%0A%7B%5BX%5C%2CY%5C%2CW%5D%7D%20%5ET%20%20%5Cleftarrow%20R%5E%7B%2D1%7D%2A%5Bx%20%5C%2C%20y%20%5C%2C%201%5D%5ET%20%20%5C%5C%0Ax%27%20%20%5Cleftarrow%20X%2FW%20%20%5C%5C%0Ay%27%20%20%5Cleftarrow%20Y%2FW%20%20%5C%5C%0Ar%5E2%20%20%5Cleftarrow%20x%27%5E2%20%2B%20y%27%5E2%20%5C%5C%0Ax%27%27%20%20%5Cleftarrow%20x%27%20%5Cfrac%7B1%20%2B%20k%5F1%20r%5E2%20%2B%20k%5F2%20r%5E4%20%2B%20k%5F3%20r%5E6%7D%7B1%20%2B%20k%5F4%20r%5E2%20%2B%20k%5F5%20r%5E4%20%2B%20k%5F6%20r%5E6%7D%0A%2B%202p%5F1%20x%27%20y%27%20%2B%20p%5F2%28r%5E2%20%2B%202%20x%27%5E2%29%20%20%2B%20s%5F1%20r%5E2%20%2B%20s%5F2%20r%5E4%5C%5C%0Ay%27%27%20%20%5Cleftarrow%20y%27%20%5Cfrac%7B1%20%2B%20k%5F1%20r%5E2%20%2B%20k%5F2%20r%5E4%20%2B%20k%5F3%20r%5E6%7D%7B1%20%2B%20k%5F4%20r%5E2%20%2B%20k%5F5%20r%5E4%20%2B%20k%5F6%20r%5E6%7D%0A%2B%20p%5F1%20%28r%5E2%20%2B%202%20y%27%5E2%29%20%2B%202%20p%5F2%20x%27%20y%27%20%2B%20s%5F3%20r%5E2%20%2B%20s%5F4%20r%5E4%20%5C%5C%0As%5Cbegin%7Bbmatrix%7D%20x%27%27%27%5C%5C%20y%27%27%27%5C%5C%201%20%5Cend%7Bbmatrix%7D%20%3D%0A%5Cvecthreethree%7BR%5F%7B33%7D%28%5Ctau%5Fx%2C%20%5Ctau%5Fy%29%7D%7B0%7D%7B%2DR%5F%7B13%7D%28%28%5Ctau%5Fx%2C%20%5Ctau%5Fy%29%7D%0A%7B0%7D%7BR%5F%7B33%7D%28%5Ctau%5Fx%2C%20%5Ctau%5Fy%29%7D%7B%2DR%5F%7B23%7D%28%5Ctau%5Fx%2C%20%5Ctau%5Fy%29%7D%0A%7B0%7D%7B0%7D%7B1%7D%20R%28%5Ctau%5Fx%2C%20%5Ctau%5Fy%29%20%5Cbegin%7Bbmatrix%7D%20x%27%27%5C%5C%20y%27%27%5C%5C%201%20%5Cend%7Bbmatrix%7D%5C%5C%0Amap%5Fx%28u%2Cv%29%20%20%5Cleftarrow%20x%27%27%27%20f%5Fx%20%2B%20c%5Fx%20%20%5C%5C%0Amap%5Fy%28u%2Cv%29%20%20%5Cleftarrow%20y%27%27%27%20f%5Fy%20%2B%20c%5Fy%0A%5Cend%7Barray%7D%0A)
	/// where ![inline formula](https://latex.codecogs.com/png.latex?%28k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%5B%2C%20k%5F3%5B%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%5B%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%20s%5F4%5B%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%5D%5D%5D%5D%29)
	/// are the distortion coefficients.
	///
	/// In case of a stereo camera, this function is called twice: once for each camera head, after
	/// #stereoRectify, which in its turn is called after #stereoCalibrate. But if the stereo camera
	/// was not calibrated, it is still possible to compute the rectification transformations directly from
	/// the fundamental matrix using #stereoRectifyUncalibrated. For each camera, the function computes
	/// homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D
	/// space. R can be computed from H as
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BR%7D%20%3D%20%5Ctexttt%7BcameraMatrix%7D%20%5E%7B%2D1%7D%20%5Ccdot%20%5Ctexttt%7BH%7D%20%5Ccdot%20%5Ctexttt%7BcameraMatrix%7D)
	/// where cameraMatrix can be chosen arbitrarily.
	///
	/// ## Parameters
	/// * cameraMatrix: Input camera matrix ![inline formula](https://latex.codecogs.com/png.latex?A%3D%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%28k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%5B%2C%20k%5F3%5B%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%5B%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%20s%5F4%5B%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%5D%5D%5D%5D%29)
	/// of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
	/// * R: Optional rectification transformation in the object space (3x3 matrix). R1 or R2 ,
	/// computed by [stereo_rectify] can be passed here. If the matrix is empty, the identity transformation
	/// is assumed. In [init_undistort_rectify_map] R assumed to be an identity matrix.
	/// * newCameraMatrix: New camera matrix ![inline formula](https://latex.codecogs.com/png.latex?A%27%3D%5Cbegin%7Bbmatrix%7D%20f%5Fx%27%20%26%200%20%26%20c%5Fx%27%5C%5C%200%20%26%20f%5Fy%27%20%26%20c%5Fy%27%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D).
	/// * size: Undistorted image size.
	/// * m1type: Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see [convert_maps]
	/// * map1: The first output map.
	/// * map2: The second output map.
	#[inline]
	pub fn init_undistort_rectify_map(camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, r: &impl ToInputArray, new_camera_matrix: &impl ToInputArray, size: core::Size, m1type: i32, map1: &mut impl ToOutputArray, map2: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		input_array_arg!(r);
		input_array_arg!(new_camera_matrix);
		output_array_arg!(map1);
		output_array_arg!(map2);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_initUndistortRectifyMap_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_Size_int_const__OutputArrayR_const__OutputArrayR(camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), r.as_raw__InputArray(), new_camera_matrix.as_raw__InputArray(), &size, m1type, map1.as_raw__OutputArray(), map2.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// initializes maps for [remap] for wide-angle
	///
	/// ## Note
	/// This alternative version of [init_wide_angle_proj_map] function uses the following default values for its arguments:
	/// * proj_type: PROJ_SPHERICAL_EQRECT
	/// * alpha: 0
	#[inline]
	pub fn init_wide_angle_proj_map_def(camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, image_size: core::Size, dest_image_width: i32, m1type: i32, map1: &mut impl ToOutputArray, map2: &mut impl ToOutputArray) -> Result<f32> {
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(map1);
		output_array_arg!(map2);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_initWideAngleProjMap_const__InputArrayR_const__InputArrayR_Size_int_int_const__OutputArrayR_const__OutputArrayR(camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), &image_size, dest_image_width, m1type, map1.as_raw__OutputArray(), map2.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// initializes maps for [remap] for wide-angle
	///
	/// ## C++ default parameters
	/// * proj_type: PROJ_SPHERICAL_EQRECT
	/// * alpha: 0
	#[inline]
	pub fn init_wide_angle_proj_map(camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, image_size: core::Size, dest_image_width: i32, m1type: i32, map1: &mut impl ToOutputArray, map2: &mut impl ToOutputArray, proj_type: crate::calib3d::UndistortTypes, alpha: f64) -> Result<f32> {
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(map1);
		output_array_arg!(map2);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_initWideAngleProjMap_const__InputArrayR_const__InputArrayR_Size_int_int_const__OutputArrayR_const__OutputArrayR_UndistortTypes_double(camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), &image_size, dest_image_width, m1type, map1.as_raw__OutputArray(), map2.as_raw__OutputArray(), proj_type, alpha, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes partial derivatives of the matrix product for each multiplied matrix.
	///
	/// ## Parameters
	/// * A: First multiplied matrix.
	/// * B: Second multiplied matrix.
	/// * dABdA: First output derivative matrix d(A\*B)/dA of size
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BA%2Erows%2AB%2Ecols%7D%20%5Ctimes%20%7BA%2Erows%2AA%2Ecols%7D) .
	/// * dABdB: Second output derivative matrix d(A\*B)/dB of size
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BA%2Erows%2AB%2Ecols%7D%20%5Ctimes%20%7BB%2Erows%2AB%2Ecols%7D) .
	///
	/// The function computes partial derivatives of the elements of the matrix product ![inline formula](https://latex.codecogs.com/png.latex?A%2AB) with regard to
	/// the elements of each of the two input matrices. The function is used to compute the Jacobian
	/// matrices in [stereo_calibrate] but can also be used in any other similar optimization function.
	#[inline]
	pub fn mat_mul_deriv(a: &impl ToInputArray, b: &impl ToInputArray, d_a_bd_a: &mut impl ToOutputArray, d_a_bd_b: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(a);
		input_array_arg!(b);
		output_array_arg!(d_a_bd_a);
		output_array_arg!(d_a_bd_b);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_matMulDeriv_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(a.as_raw__InputArray(), b.as_raw__InputArray(), d_a_bd_a.as_raw__OutputArray(), d_a_bd_b.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Projects 3D points to an image plane.
	///
	/// ## Parameters
	/// * objectPoints: Array of object points expressed wrt. the world coordinate frame. A 3xN/Nx3
	/// 1-channel or 1xN/Nx1 3-channel (or vector\<Point3f\> ), where N is the number of points in the view.
	/// * rvec: The rotation vector ([Rodrigues]) that, together with tvec, performs a change of
	/// basis from world to camera coordinate system, see [calibrateCamera] for details.
	/// * tvec: The translation vector, see parameter description above.
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs) . If the vector is empty, the zero distortion coefficients are assumed.
	/// * imagePoints: Output array of image points, 1xN/Nx1 2-channel, or
	/// vector\<Point2f\> .
	/// * jacobian: Optional output 2Nx(10+\<numDistCoeffs\>) jacobian matrix of derivatives of image
	/// points with respect to components of the rotation vector, translation vector, focal lengths,
	/// coordinates of the principal point and the distortion coefficients. In the old interface different
	/// components of the jacobian are returned via different output parameters.
	/// * aspectRatio: Optional "fixed aspect ratio" parameter. If the parameter is not 0, the
	/// function assumes that the aspect ratio (![inline formula](https://latex.codecogs.com/png.latex?f%5Fx%20%2F%20f%5Fy)) is fixed and correspondingly adjusts the
	/// jacobian matrix.
	///
	/// The function computes the 2D projections of 3D points to the image plane, given intrinsic and
	/// extrinsic camera parameters. Optionally, the function computes Jacobians -matrices of partial
	/// derivatives of image points coordinates (as functions of all the input parameters) with respect to
	/// the particular parameters, intrinsic and/or extrinsic. The Jacobians are used during the global
	/// optimization in [calibrateCamera], [solvePnP], and [stereoCalibrate]. The function itself
	/// can also be used to compute a re-projection error, given the current intrinsic and extrinsic
	/// parameters.
	///
	///
	/// Note: By setting rvec = tvec = ![inline formula](https://latex.codecogs.com/png.latex?%5B0%2C%200%2C%200%5D), or by setting cameraMatrix to a 3x3 identity matrix,
	/// or by passing zero distortion coefficients, one can get various useful partial cases of the
	/// function. This means, one can compute the distorted coordinates for a sparse set of points or apply
	/// a perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup.
	///
	/// ## Note
	/// This alternative version of [project_points] function uses the following default values for its arguments:
	/// * jacobian: noArray()
	/// * aspect_ratio: 0
	#[inline]
	pub fn project_points_def(object_points: &impl ToInputArray, rvec: &impl ToInputArray, tvec: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, image_points: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(object_points);
		input_array_arg!(rvec);
		input_array_arg!(tvec);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(image_points);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_projectPoints_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), rvec.as_raw__InputArray(), tvec.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), image_points.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Projects 3D points to an image plane.
	///
	/// ## Parameters
	/// * objectPoints: Array of object points expressed wrt. the world coordinate frame. A 3xN/Nx3
	/// 1-channel or 1xN/Nx1 3-channel (or vector\<Point3f\> ), where N is the number of points in the view.
	/// * rvec: The rotation vector ([Rodrigues]) that, together with tvec, performs a change of
	/// basis from world to camera coordinate system, see [calibrateCamera] for details.
	/// * tvec: The translation vector, see parameter description above.
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs) . If the vector is empty, the zero distortion coefficients are assumed.
	/// * imagePoints: Output array of image points, 1xN/Nx1 2-channel, or
	/// vector\<Point2f\> .
	/// * jacobian: Optional output 2Nx(10+\<numDistCoeffs\>) jacobian matrix of derivatives of image
	/// points with respect to components of the rotation vector, translation vector, focal lengths,
	/// coordinates of the principal point and the distortion coefficients. In the old interface different
	/// components of the jacobian are returned via different output parameters.
	/// * aspectRatio: Optional "fixed aspect ratio" parameter. If the parameter is not 0, the
	/// function assumes that the aspect ratio (![inline formula](https://latex.codecogs.com/png.latex?f%5Fx%20%2F%20f%5Fy)) is fixed and correspondingly adjusts the
	/// jacobian matrix.
	///
	/// The function computes the 2D projections of 3D points to the image plane, given intrinsic and
	/// extrinsic camera parameters. Optionally, the function computes Jacobians -matrices of partial
	/// derivatives of image points coordinates (as functions of all the input parameters) with respect to
	/// the particular parameters, intrinsic and/or extrinsic. The Jacobians are used during the global
	/// optimization in [calibrateCamera], [solvePnP], and [stereoCalibrate]. The function itself
	/// can also be used to compute a re-projection error, given the current intrinsic and extrinsic
	/// parameters.
	///
	///
	/// Note: By setting rvec = tvec = ![inline formula](https://latex.codecogs.com/png.latex?%5B0%2C%200%2C%200%5D), or by setting cameraMatrix to a 3x3 identity matrix,
	/// or by passing zero distortion coefficients, one can get various useful partial cases of the
	/// function. This means, one can compute the distorted coordinates for a sparse set of points or apply
	/// a perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup.
	///
	/// ## C++ default parameters
	/// * jacobian: noArray()
	/// * aspect_ratio: 0
	#[inline]
	pub fn project_points(object_points: &impl ToInputArray, rvec: &impl ToInputArray, tvec: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, image_points: &mut impl ToOutputArray, jacobian: &mut impl ToOutputArray, aspect_ratio: f64) -> Result<()> {
		input_array_arg!(object_points);
		input_array_arg!(rvec);
		input_array_arg!(tvec);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(image_points);
		output_array_arg!(jacobian);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_projectPoints_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_double(object_points.as_raw__InputArray(), rvec.as_raw__InputArray(), tvec.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), image_points.as_raw__OutputArray(), jacobian.as_raw__OutputArray(), aspect_ratio, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Recovers the relative camera rotation and the translation from corresponding points in two images from two different cameras, using cheirality check. Returns the number of
	/// inliers that pass the check.
	///
	/// ## Parameters
	/// * points1: Array of N 2D points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * cameraMatrix1: Input/output camera matrix for the first camera, the same as in
	/// [calibrateCamera]. Furthermore, for the stereo case, additional flags may be used, see below.
	/// * distCoeffs1: Input/output vector of distortion coefficients, the same as in
	/// [calibrateCamera].
	/// * cameraMatrix2: Input/output camera matrix for the first camera, the same as in
	/// [calibrateCamera]. Furthermore, for the stereo case, additional flags may be used, see below.
	/// * distCoeffs2: Input/output vector of distortion coefficients, the same as in
	/// [calibrateCamera].
	/// * E: The output essential matrix.
	/// * R: Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
	/// that performs a change of basis from the first camera's coordinate system to the second camera's
	/// coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
	/// described below.
	/// * t: Output translation vector. This vector is obtained by [decomposeEssentialMat] and
	/// therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
	/// length.
	/// * method: Method for computing an essential matrix.
	/// *   [RANSAC] for the RANSAC algorithm.
	/// *   [LMEDS] for the LMedS algorithm.
	/// * prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
	/// confidence (probability) that the estimated matrix is correct.
	/// * threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * mask: Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
	/// inliers in points1 and points2 for then given essential matrix E. Only these inliers will be used to
	/// recover pose. In the output mask only inliers which pass the cheirality check.
	///
	/// This function decomposes an essential matrix using [decomposeEssentialMat] and then verifies
	/// possible pose hypotheses by doing cheirality check. The cheirality check means that the
	/// triangulated 3D points should have positive depth. Some details can be found in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03).
	///
	/// This function can be used to process the output E and mask from [findEssentialMat]. In this
	/// scenario, points1 and points2 are the same input for findEssentialMat.:
	/// ```C++
	///    // Example. Estimation of fundamental matrix using the RANSAC algorithm
	///    int point_count = 100;
	///    vector<Point2f> points1(point_count);
	///    vector<Point2f> points2(point_count);
	///
	///    // initialize the points here ...
	///    for( int i = 0; i < point_count; i++ )
	///    {
	///        points1[i] = ...;
	///        points2[i] = ...;
	///    }
	///
	///    // Input: camera calibration of both cameras, for example using intrinsic chessboard calibration.
	///    Mat cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2;
	///
	///    // Output: Essential matrix, relative rotation and relative translation.
	///    Mat E, R, t, mask;
	///
	///    recoverPose(points1, points2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, E, R, t, mask);
	/// ```
	///
	///
	/// ## Note
	/// This alternative version of [recover_pose_2_cameras] function uses the following default values for its arguments:
	/// * method: cv::RANSAC
	/// * prob: 0.999
	/// * threshold: 1.0
	/// * mask: noArray()
	#[inline]
	pub fn recover_pose_2_cameras_def(points1: &impl ToInputArray, points2: &impl ToInputArray, camera_matrix1: &impl ToInputArray, dist_coeffs1: &impl ToInputArray, camera_matrix2: &impl ToInputArray, dist_coeffs2: &impl ToInputArray, e: &mut impl ToOutputArray, r: &mut impl ToOutputArray, t: &mut impl ToOutputArray) -> Result<i32> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		input_array_arg!(camera_matrix1);
		input_array_arg!(dist_coeffs1);
		input_array_arg!(camera_matrix2);
		input_array_arg!(dist_coeffs2);
		output_array_arg!(e);
		output_array_arg!(r);
		output_array_arg!(t);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_recoverPose_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), camera_matrix1.as_raw__InputArray(), dist_coeffs1.as_raw__InputArray(), camera_matrix2.as_raw__InputArray(), dist_coeffs2.as_raw__InputArray(), e.as_raw__OutputArray(), r.as_raw__OutputArray(), t.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Recovers the relative camera rotation and the translation from corresponding points in two images from two different cameras, using cheirality check. Returns the number of
	/// inliers that pass the check.
	///
	/// ## Parameters
	/// * points1: Array of N 2D points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * cameraMatrix1: Input/output camera matrix for the first camera, the same as in
	/// [calibrateCamera]. Furthermore, for the stereo case, additional flags may be used, see below.
	/// * distCoeffs1: Input/output vector of distortion coefficients, the same as in
	/// [calibrateCamera].
	/// * cameraMatrix2: Input/output camera matrix for the first camera, the same as in
	/// [calibrateCamera]. Furthermore, for the stereo case, additional flags may be used, see below.
	/// * distCoeffs2: Input/output vector of distortion coefficients, the same as in
	/// [calibrateCamera].
	/// * E: The output essential matrix.
	/// * R: Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
	/// that performs a change of basis from the first camera's coordinate system to the second camera's
	/// coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
	/// described below.
	/// * t: Output translation vector. This vector is obtained by [decomposeEssentialMat] and
	/// therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
	/// length.
	/// * method: Method for computing an essential matrix.
	/// *   [RANSAC] for the RANSAC algorithm.
	/// *   [LMEDS] for the LMedS algorithm.
	/// * prob: Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
	/// confidence (probability) that the estimated matrix is correct.
	/// * threshold: Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
	/// line in pixels, beyond which the point is considered an outlier and is not used for computing the
	/// final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
	/// point localization, image resolution, and the image noise.
	/// * mask: Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
	/// inliers in points1 and points2 for then given essential matrix E. Only these inliers will be used to
	/// recover pose. In the output mask only inliers which pass the cheirality check.
	///
	/// This function decomposes an essential matrix using [decomposeEssentialMat] and then verifies
	/// possible pose hypotheses by doing cheirality check. The cheirality check means that the
	/// triangulated 3D points should have positive depth. Some details can be found in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03).
	///
	/// This function can be used to process the output E and mask from [findEssentialMat]. In this
	/// scenario, points1 and points2 are the same input for findEssentialMat.:
	/// ```C++
	///    // Example. Estimation of fundamental matrix using the RANSAC algorithm
	///    int point_count = 100;
	///    vector<Point2f> points1(point_count);
	///    vector<Point2f> points2(point_count);
	///
	///    // initialize the points here ...
	///    for( int i = 0; i < point_count; i++ )
	///    {
	///        points1[i] = ...;
	///        points2[i] = ...;
	///    }
	///
	///    // Input: camera calibration of both cameras, for example using intrinsic chessboard calibration.
	///    Mat cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2;
	///
	///    // Output: Essential matrix, relative rotation and relative translation.
	///    Mat E, R, t, mask;
	///
	///    recoverPose(points1, points2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, E, R, t, mask);
	/// ```
	///
	///
	/// ## C++ default parameters
	/// * method: cv::RANSAC
	/// * prob: 0.999
	/// * threshold: 1.0
	/// * mask: noArray()
	#[inline]
	pub fn recover_pose_2_cameras(points1: &impl ToInputArray, points2: &impl ToInputArray, camera_matrix1: &impl ToInputArray, dist_coeffs1: &impl ToInputArray, camera_matrix2: &impl ToInputArray, dist_coeffs2: &impl ToInputArray, e: &mut impl ToOutputArray, r: &mut impl ToOutputArray, t: &mut impl ToOutputArray, method: i32, prob: f64, threshold: f64, mask: &mut impl ToInputOutputArray) -> Result<i32> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		input_array_arg!(camera_matrix1);
		input_array_arg!(dist_coeffs1);
		input_array_arg!(camera_matrix2);
		input_array_arg!(dist_coeffs2);
		output_array_arg!(e);
		output_array_arg!(r);
		output_array_arg!(t);
		input_output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_recoverPose_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_double_double_const__InputOutputArrayR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), camera_matrix1.as_raw__InputArray(), dist_coeffs1.as_raw__InputArray(), camera_matrix2.as_raw__InputArray(), dist_coeffs2.as_raw__InputArray(), e.as_raw__OutputArray(), r.as_raw__OutputArray(), t.as_raw__OutputArray(), method, prob, threshold, mask.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Recovers the relative camera rotation and the translation from an estimated essential
	/// matrix and the corresponding points in two images, using chirality check. Returns the number of
	/// inliers that pass the check.
	///
	/// ## Parameters
	/// * E: The input essential matrix.
	/// * points1: Array of N 2D points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// Note that this function assumes that points1 and points2 are feature points from cameras with the
	/// same camera intrinsic matrix.
	/// * R: Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
	/// that performs a change of basis from the first camera's coordinate system to the second camera's
	/// coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
	/// described below.
	/// * t: Output translation vector. This vector is obtained by [decomposeEssentialMat] and
	/// therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
	/// length.
	/// * mask: Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
	/// inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to
	/// recover pose. In the output mask only inliers which pass the chirality check.
	///
	/// This function decomposes an essential matrix using [decomposeEssentialMat] and then verifies
	/// possible pose hypotheses by doing chirality check. The chirality check means that the
	/// triangulated 3D points should have positive depth. Some details can be found in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03).
	///
	/// This function can be used to process the output E and mask from [findEssentialMat]. In this
	/// scenario, points1 and points2 are the same input for [find_essential_mat] :
	/// ```C++
	///    // Example. Estimation of fundamental matrix using the RANSAC algorithm
	///    int point_count = 100;
	///    vector<Point2f> points1(point_count);
	///    vector<Point2f> points2(point_count);
	///
	///    // initialize the points here ...
	///    for( int i = 0; i < point_count; i++ )
	///    {
	///        points1[i] = ...;
	///        points2[i] = ...;
	///    }
	///
	///    // cametra matrix with both focal lengths = 1, and principal point = (0, 0)
	///    Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
	///
	///    Mat E, R, t, mask;
	///
	///    E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
	///    recoverPose(E, points1, points2, cameraMatrix, R, t, mask);
	/// ```
	///
	///
	/// ## Note
	/// This alternative version of [recover_pose_estimated] function uses the following default values for its arguments:
	/// * mask: noArray()
	#[inline]
	pub fn recover_pose_estimated_def(e: &impl ToInputArray, points1: &impl ToInputArray, points2: &impl ToInputArray, camera_matrix: &impl ToInputArray, r: &mut impl ToOutputArray, t: &mut impl ToOutputArray) -> Result<i32> {
		input_array_arg!(e);
		input_array_arg!(points1);
		input_array_arg!(points2);
		input_array_arg!(camera_matrix);
		output_array_arg!(r);
		output_array_arg!(t);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_recoverPose_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(e.as_raw__InputArray(), points1.as_raw__InputArray(), points2.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), r.as_raw__OutputArray(), t.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Recovers the relative camera rotation and the translation from an estimated essential
	/// matrix and the corresponding points in two images, using chirality check. Returns the number of
	/// inliers that pass the check.
	///
	/// ## Parameters
	/// * E: The input essential matrix.
	/// * points1: Array of N 2D points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// Note that this function assumes that points1 and points2 are feature points from cameras with the
	/// same camera intrinsic matrix.
	/// * R: Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
	/// that performs a change of basis from the first camera's coordinate system to the second camera's
	/// coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
	/// described below.
	/// * t: Output translation vector. This vector is obtained by [decomposeEssentialMat] and
	/// therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
	/// length.
	/// * mask: Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
	/// inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to
	/// recover pose. In the output mask only inliers which pass the chirality check.
	///
	/// This function decomposes an essential matrix using [decomposeEssentialMat] and then verifies
	/// possible pose hypotheses by doing chirality check. The chirality check means that the
	/// triangulated 3D points should have positive depth. Some details can be found in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03).
	///
	/// This function can be used to process the output E and mask from [findEssentialMat]. In this
	/// scenario, points1 and points2 are the same input for [find_essential_mat] :
	/// ```C++
	///    // Example. Estimation of fundamental matrix using the RANSAC algorithm
	///    int point_count = 100;
	///    vector<Point2f> points1(point_count);
	///    vector<Point2f> points2(point_count);
	///
	///    // initialize the points here ...
	///    for( int i = 0; i < point_count; i++ )
	///    {
	///        points1[i] = ...;
	///        points2[i] = ...;
	///    }
	///
	///    // cametra matrix with both focal lengths = 1, and principal point = (0, 0)
	///    Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
	///
	///    Mat E, R, t, mask;
	///
	///    E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
	///    recoverPose(E, points1, points2, cameraMatrix, R, t, mask);
	/// ```
	///
	///
	/// ## C++ default parameters
	/// * mask: noArray()
	#[inline]
	pub fn recover_pose_estimated(e: &impl ToInputArray, points1: &impl ToInputArray, points2: &impl ToInputArray, camera_matrix: &impl ToInputArray, r: &mut impl ToOutputArray, t: &mut impl ToOutputArray, mask: &mut impl ToInputOutputArray) -> Result<i32> {
		input_array_arg!(e);
		input_array_arg!(points1);
		input_array_arg!(points2);
		input_array_arg!(camera_matrix);
		output_array_arg!(r);
		output_array_arg!(t);
		input_output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_recoverPose_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__InputOutputArrayR(e.as_raw__InputArray(), points1.as_raw__InputArray(), points2.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), r.as_raw__OutputArray(), t.as_raw__OutputArray(), mask.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Recovers the relative camera rotation and the translation from an estimated essential
	/// matrix and the corresponding points in two images, using chirality check. Returns the number of
	/// inliers that pass the check.
	///
	/// ## Parameters
	/// * E: The input essential matrix.
	/// * points1: Array of N 2D points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// Note that this function assumes that points1 and points2 are feature points from cameras with the
	/// same camera intrinsic matrix.
	/// * R: Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
	/// that performs a change of basis from the first camera's coordinate system to the second camera's
	/// coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
	/// described below.
	/// * t: Output translation vector. This vector is obtained by [decomposeEssentialMat] and
	/// therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
	/// length.
	/// * mask: Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
	/// inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to
	/// recover pose. In the output mask only inliers which pass the chirality check.
	///
	/// This function decomposes an essential matrix using [decomposeEssentialMat] and then verifies
	/// possible pose hypotheses by doing chirality check. The chirality check means that the
	/// triangulated 3D points should have positive depth. Some details can be found in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03).
	///
	/// This function can be used to process the output E and mask from [findEssentialMat]. In this
	/// scenario, points1 and points2 are the same input for [find_essential_mat] :
	/// ```C++
	///    // Example. Estimation of fundamental matrix using the RANSAC algorithm
	///    int point_count = 100;
	///    vector<Point2f> points1(point_count);
	///    vector<Point2f> points2(point_count);
	///
	///    // initialize the points here ...
	///    for( int i = 0; i < point_count; i++ )
	///    {
	///        points1[i] = ...;
	///        points2[i] = ...;
	///    }
	///
	///    // cametra matrix with both focal lengths = 1, and principal point = (0, 0)
	///    Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
	///
	///    Mat E, R, t, mask;
	///
	///    E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
	///    recoverPose(E, points1, points2, cameraMatrix, R, t, mask);
	/// ```
	///
	///
	/// ## Overloaded parameters
	///
	/// * E: The input essential matrix.
	/// * points1: Array of N 2D points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1.
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// Note that this function assumes that points1 and points2 are feature points from cameras with the
	/// same camera intrinsic matrix.
	/// * R: Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
	/// that performs a change of basis from the first camera's coordinate system to the second camera's
	/// coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
	/// description below.
	/// * t: Output translation vector. This vector is obtained by [decomposeEssentialMat] and
	/// therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
	/// length.
	/// * distanceThresh: threshold distance which is used to filter out far away points (i.e. infinite
	/// points).
	/// * mask: Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
	/// inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to
	/// recover pose. In the output mask only inliers which pass the chirality check.
	/// * triangulatedPoints: 3D points which were reconstructed by triangulation.
	///
	/// This function differs from the one above that it outputs the triangulated 3D point that are used for
	/// the chirality check.
	///
	/// ## Note
	/// This alternative version of [recover_pose_triangulated] function uses the following default values for its arguments:
	/// * mask: noArray()
	/// * triangulated_points: noArray()
	#[inline]
	pub fn recover_pose_triangulated_def(e: &impl ToInputArray, points1: &impl ToInputArray, points2: &impl ToInputArray, camera_matrix: &impl ToInputArray, r: &mut impl ToOutputArray, t: &mut impl ToOutputArray, distance_thresh: f64) -> Result<i32> {
		input_array_arg!(e);
		input_array_arg!(points1);
		input_array_arg!(points2);
		input_array_arg!(camera_matrix);
		output_array_arg!(r);
		output_array_arg!(t);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_recoverPose_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_double(e.as_raw__InputArray(), points1.as_raw__InputArray(), points2.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), r.as_raw__OutputArray(), t.as_raw__OutputArray(), distance_thresh, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Recovers the relative camera rotation and the translation from an estimated essential
	/// matrix and the corresponding points in two images, using chirality check. Returns the number of
	/// inliers that pass the check.
	///
	/// ## Parameters
	/// * E: The input essential matrix.
	/// * points1: Array of N 2D points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// Note that this function assumes that points1 and points2 are feature points from cameras with the
	/// same camera intrinsic matrix.
	/// * R: Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
	/// that performs a change of basis from the first camera's coordinate system to the second camera's
	/// coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
	/// described below.
	/// * t: Output translation vector. This vector is obtained by [decomposeEssentialMat] and
	/// therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
	/// length.
	/// * mask: Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
	/// inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to
	/// recover pose. In the output mask only inliers which pass the chirality check.
	///
	/// This function decomposes an essential matrix using [decomposeEssentialMat] and then verifies
	/// possible pose hypotheses by doing chirality check. The chirality check means that the
	/// triangulated 3D points should have positive depth. Some details can be found in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03).
	///
	/// This function can be used to process the output E and mask from [findEssentialMat]. In this
	/// scenario, points1 and points2 are the same input for [find_essential_mat] :
	/// ```C++
	///    // Example. Estimation of fundamental matrix using the RANSAC algorithm
	///    int point_count = 100;
	///    vector<Point2f> points1(point_count);
	///    vector<Point2f> points2(point_count);
	///
	///    // initialize the points here ...
	///    for( int i = 0; i < point_count; i++ )
	///    {
	///        points1[i] = ...;
	///        points2[i] = ...;
	///    }
	///
	///    // cametra matrix with both focal lengths = 1, and principal point = (0, 0)
	///    Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
	///
	///    Mat E, R, t, mask;
	///
	///    E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
	///    recoverPose(E, points1, points2, cameraMatrix, R, t, mask);
	/// ```
	///
	///
	/// ## Overloaded parameters
	///
	/// * E: The input essential matrix.
	/// * points1: Array of N 2D points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1.
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// Note that this function assumes that points1 and points2 are feature points from cameras with the
	/// same camera intrinsic matrix.
	/// * R: Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
	/// that performs a change of basis from the first camera's coordinate system to the second camera's
	/// coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
	/// description below.
	/// * t: Output translation vector. This vector is obtained by [decomposeEssentialMat] and
	/// therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
	/// length.
	/// * distanceThresh: threshold distance which is used to filter out far away points (i.e. infinite
	/// points).
	/// * mask: Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
	/// inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to
	/// recover pose. In the output mask only inliers which pass the chirality check.
	/// * triangulatedPoints: 3D points which were reconstructed by triangulation.
	///
	/// This function differs from the one above that it outputs the triangulated 3D point that are used for
	/// the chirality check.
	///
	/// ## C++ default parameters
	/// * mask: noArray()
	/// * triangulated_points: noArray()
	#[inline]
	pub fn recover_pose_triangulated(e: &impl ToInputArray, points1: &impl ToInputArray, points2: &impl ToInputArray, camera_matrix: &impl ToInputArray, r: &mut impl ToOutputArray, t: &mut impl ToOutputArray, distance_thresh: f64, mask: &mut impl ToInputOutputArray, triangulated_points: &mut impl ToOutputArray) -> Result<i32> {
		input_array_arg!(e);
		input_array_arg!(points1);
		input_array_arg!(points2);
		input_array_arg!(camera_matrix);
		output_array_arg!(r);
		output_array_arg!(t);
		input_output_array_arg!(mask);
		output_array_arg!(triangulated_points);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_recoverPose_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_double_const__InputOutputArrayR_const__OutputArrayR(e.as_raw__InputArray(), points1.as_raw__InputArray(), points2.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), r.as_raw__OutputArray(), t.as_raw__OutputArray(), distance_thresh, mask.as_raw__InputOutputArray(), triangulated_points.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Recovers the relative camera rotation and the translation from an estimated essential
	/// matrix and the corresponding points in two images, using chirality check. Returns the number of
	/// inliers that pass the check.
	///
	/// ## Parameters
	/// * E: The input essential matrix.
	/// * points1: Array of N 2D points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// Note that this function assumes that points1 and points2 are feature points from cameras with the
	/// same camera intrinsic matrix.
	/// * R: Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
	/// that performs a change of basis from the first camera's coordinate system to the second camera's
	/// coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
	/// described below.
	/// * t: Output translation vector. This vector is obtained by [decomposeEssentialMat] and
	/// therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
	/// length.
	/// * mask: Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
	/// inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to
	/// recover pose. In the output mask only inliers which pass the chirality check.
	///
	/// This function decomposes an essential matrix using [decomposeEssentialMat] and then verifies
	/// possible pose hypotheses by doing chirality check. The chirality check means that the
	/// triangulated 3D points should have positive depth. Some details can be found in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03).
	///
	/// This function can be used to process the output E and mask from [findEssentialMat]. In this
	/// scenario, points1 and points2 are the same input for [find_essential_mat] :
	/// ```C++
	///    // Example. Estimation of fundamental matrix using the RANSAC algorithm
	///    int point_count = 100;
	///    vector<Point2f> points1(point_count);
	///    vector<Point2f> points2(point_count);
	///
	///    // initialize the points here ...
	///    for( int i = 0; i < point_count; i++ )
	///    {
	///        points1[i] = ...;
	///        points2[i] = ...;
	///    }
	///
	///    // cametra matrix with both focal lengths = 1, and principal point = (0, 0)
	///    Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
	///
	///    Mat E, R, t, mask;
	///
	///    E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
	///    recoverPose(E, points1, points2, cameraMatrix, R, t, mask);
	/// ```
	///
	///
	/// ## Overloaded parameters
	///
	/// * E: The input essential matrix.
	/// * points1: Array of N 2D points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * R: Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
	/// that performs a change of basis from the first camera's coordinate system to the second camera's
	/// coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
	/// description below.
	/// * t: Output translation vector. This vector is obtained by [decomposeEssentialMat] and
	/// therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
	/// length.
	/// * focal: Focal length of the camera. Note that this function assumes that points1 and points2
	/// are feature points from cameras with same focal length and principal point.
	/// * pp: principal point of the camera.
	/// * mask: Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
	/// inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to
	/// recover pose. In the output mask only inliers which pass the chirality check.
	///
	/// This function differs from the one above that it computes camera intrinsic matrix from focal length and
	/// principal point:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?A%20%3D%0A%5Cbegin%7Bbmatrix%7D%0Af%20%26%200%20%26%20x%5F%7Bpp%7D%20%20%5C%5C%0A0%20%26%20f%20%26%20y%5F%7Bpp%7D%20%20%5C%5C%0A0%20%26%200%20%26%201%0A%5Cend%7Bbmatrix%7D)
	///
	/// ## Note
	/// This alternative version of [recover_pose] function uses the following default values for its arguments:
	/// * focal: 1.0
	/// * pp: Point2d(0,0)
	/// * mask: noArray()
	#[inline]
	pub fn recover_pose_def(e: &impl ToInputArray, points1: &impl ToInputArray, points2: &impl ToInputArray, r: &mut impl ToOutputArray, t: &mut impl ToOutputArray) -> Result<i32> {
		input_array_arg!(e);
		input_array_arg!(points1);
		input_array_arg!(points2);
		output_array_arg!(r);
		output_array_arg!(t);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_recoverPose_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(e.as_raw__InputArray(), points1.as_raw__InputArray(), points2.as_raw__InputArray(), r.as_raw__OutputArray(), t.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Recovers the relative camera rotation and the translation from an estimated essential
	/// matrix and the corresponding points in two images, using chirality check. Returns the number of
	/// inliers that pass the check.
	///
	/// ## Parameters
	/// * E: The input essential matrix.
	/// * points1: Array of N 2D points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * cameraMatrix: Camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// Note that this function assumes that points1 and points2 are feature points from cameras with the
	/// same camera intrinsic matrix.
	/// * R: Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
	/// that performs a change of basis from the first camera's coordinate system to the second camera's
	/// coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
	/// described below.
	/// * t: Output translation vector. This vector is obtained by [decomposeEssentialMat] and
	/// therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
	/// length.
	/// * mask: Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
	/// inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to
	/// recover pose. In the output mask only inliers which pass the chirality check.
	///
	/// This function decomposes an essential matrix using [decomposeEssentialMat] and then verifies
	/// possible pose hypotheses by doing chirality check. The chirality check means that the
	/// triangulated 3D points should have positive depth. Some details can be found in [Nister03](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Nister03).
	///
	/// This function can be used to process the output E and mask from [findEssentialMat]. In this
	/// scenario, points1 and points2 are the same input for [find_essential_mat] :
	/// ```C++
	///    // Example. Estimation of fundamental matrix using the RANSAC algorithm
	///    int point_count = 100;
	///    vector<Point2f> points1(point_count);
	///    vector<Point2f> points2(point_count);
	///
	///    // initialize the points here ...
	///    for( int i = 0; i < point_count; i++ )
	///    {
	///        points1[i] = ...;
	///        points2[i] = ...;
	///    }
	///
	///    // cametra matrix with both focal lengths = 1, and principal point = (0, 0)
	///    Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
	///
	///    Mat E, R, t, mask;
	///
	///    E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
	///    recoverPose(E, points1, points2, cameraMatrix, R, t, mask);
	/// ```
	///
	///
	/// ## Overloaded parameters
	///
	/// * E: The input essential matrix.
	/// * points1: Array of N 2D points from the first image. The point coordinates should be
	/// floating-point (single or double precision).
	/// * points2: Array of the second image points of the same size and format as points1 .
	/// * R: Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
	/// that performs a change of basis from the first camera's coordinate system to the second camera's
	/// coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
	/// description below.
	/// * t: Output translation vector. This vector is obtained by [decomposeEssentialMat] and
	/// therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
	/// length.
	/// * focal: Focal length of the camera. Note that this function assumes that points1 and points2
	/// are feature points from cameras with same focal length and principal point.
	/// * pp: principal point of the camera.
	/// * mask: Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
	/// inliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to
	/// recover pose. In the output mask only inliers which pass the chirality check.
	///
	/// This function differs from the one above that it computes camera intrinsic matrix from focal length and
	/// principal point:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?A%20%3D%0A%5Cbegin%7Bbmatrix%7D%0Af%20%26%200%20%26%20x%5F%7Bpp%7D%20%20%5C%5C%0A0%20%26%20f%20%26%20y%5F%7Bpp%7D%20%20%5C%5C%0A0%20%26%200%20%26%201%0A%5Cend%7Bbmatrix%7D)
	///
	/// ## C++ default parameters
	/// * focal: 1.0
	/// * pp: Point2d(0,0)
	/// * mask: noArray()
	#[inline]
	pub fn recover_pose(e: &impl ToInputArray, points1: &impl ToInputArray, points2: &impl ToInputArray, r: &mut impl ToOutputArray, t: &mut impl ToOutputArray, focal: f64, pp: core::Point2d, mask: &mut impl ToInputOutputArray) -> Result<i32> {
		input_array_arg!(e);
		input_array_arg!(points1);
		input_array_arg!(points2);
		output_array_arg!(r);
		output_array_arg!(t);
		input_output_array_arg!(mask);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_recoverPose_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_double_Point2d_const__InputOutputArrayR(e.as_raw__InputArray(), points1.as_raw__InputArray(), points2.as_raw__InputArray(), r.as_raw__OutputArray(), t.as_raw__OutputArray(), focal, &pp, mask.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// computes the rectification transformations for 3-head camera, where all the heads are on the same line.
	#[inline]
	pub fn rectify3_collinear(camera_matrix1: &impl ToInputArray, dist_coeffs1: &impl ToInputArray, camera_matrix2: &impl ToInputArray, dist_coeffs2: &impl ToInputArray, camera_matrix3: &impl ToInputArray, dist_coeffs3: &impl ToInputArray, imgpt1: &impl ToInputArray, imgpt3: &impl ToInputArray, image_size: core::Size, r12: &impl ToInputArray, t12: &impl ToInputArray, r13: &impl ToInputArray, t13: &impl ToInputArray, r1: &mut impl ToOutputArray, r2: &mut impl ToOutputArray, r3: &mut impl ToOutputArray, p1: &mut impl ToOutputArray, p2: &mut impl ToOutputArray, p3: &mut impl ToOutputArray, q: &mut impl ToOutputArray, alpha: f64, new_img_size: core::Size, roi1: &mut core::Rect, roi2: &mut core::Rect, flags: i32) -> Result<f32> {
		input_array_arg!(camera_matrix1);
		input_array_arg!(dist_coeffs1);
		input_array_arg!(camera_matrix2);
		input_array_arg!(dist_coeffs2);
		input_array_arg!(camera_matrix3);
		input_array_arg!(dist_coeffs3);
		input_array_arg!(imgpt1);
		input_array_arg!(imgpt3);
		input_array_arg!(r12);
		input_array_arg!(t12);
		input_array_arg!(r13);
		input_array_arg!(t13);
		output_array_arg!(r1);
		output_array_arg!(r2);
		output_array_arg!(r3);
		output_array_arg!(p1);
		output_array_arg!(p2);
		output_array_arg!(p3);
		output_array_arg!(q);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_rectify3Collinear_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_Size_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_double_Size_RectX_RectX_int(camera_matrix1.as_raw__InputArray(), dist_coeffs1.as_raw__InputArray(), camera_matrix2.as_raw__InputArray(), dist_coeffs2.as_raw__InputArray(), camera_matrix3.as_raw__InputArray(), dist_coeffs3.as_raw__InputArray(), imgpt1.as_raw__InputArray(), imgpt3.as_raw__InputArray(), &image_size, r12.as_raw__InputArray(), t12.as_raw__InputArray(), r13.as_raw__InputArray(), t13.as_raw__InputArray(), r1.as_raw__OutputArray(), r2.as_raw__OutputArray(), r3.as_raw__OutputArray(), p1.as_raw__OutputArray(), p2.as_raw__OutputArray(), p3.as_raw__OutputArray(), q.as_raw__OutputArray(), alpha, &new_img_size, roi1, roi2, flags, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Reprojects a disparity image to 3D space.
	///
	/// ## Parameters
	/// * disparity: Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit
	/// floating-point disparity image. The values of 8-bit / 16-bit signed formats are assumed to have no
	/// fractional bits. If the disparity is 16-bit signed format, as computed by [StereoBM] or
	/// [StereoSGBM] and maybe other algorithms, it should be divided by 16 (and scaled to float) before
	/// being used here.
	/// * _3dImage: Output 3-channel floating-point image of the same size as disparity. Each element of
	/// _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity map. If one
	/// uses Q obtained by [stereoRectify], then the returned points are represented in the first
	/// camera's rectified coordinate system.
	/// * Q: ![inline formula](https://latex.codecogs.com/png.latex?4%20%5Ctimes%204) perspective transformation matrix that can be obtained with
	/// [stereoRectify].
	/// * handleMissingValues: Indicates, whether the function should handle missing values (i.e.
	/// points where the disparity was not computed). If handleMissingValues=true, then pixels with the
	/// minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed
	/// to 3D points with a very large Z value (currently set to 10000).
	/// * ddepth: The optional output array depth. If it is -1, the output image will have CV_32F
	/// depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.
	///
	/// The function transforms a single-channel disparity map to a 3-channel image representing a 3D
	/// surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it
	/// computes:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0AX%20%5C%5C%0AY%20%5C%5C%0AZ%20%5C%5C%0AW%0A%5Cend%7Bbmatrix%7D%20%3D%20Q%20%5Cbegin%7Bbmatrix%7D%0Ax%20%5C%5C%0Ay%20%5C%5C%0A%5Ctexttt%7Bdisparity%7D%20%28x%2Cy%29%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%2E)
	/// ## See also
	/// To reproject a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform.
	///
	/// ## Note
	/// This alternative version of [reproject_image_to_3d] function uses the following default values for its arguments:
	/// * handle_missing_values: false
	/// * ddepth: -1
	#[inline]
	pub fn reproject_image_to_3d_def(disparity: &impl ToInputArray, _3d_image: &mut impl ToOutputArray, q: &impl ToInputArray) -> Result<()> {
		input_array_arg!(disparity);
		output_array_arg!(_3d_image);
		input_array_arg!(q);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_reprojectImageTo3D_const__InputArrayR_const__OutputArrayR_const__InputArrayR(disparity.as_raw__InputArray(), _3d_image.as_raw__OutputArray(), q.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Reprojects a disparity image to 3D space.
	///
	/// ## Parameters
	/// * disparity: Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit
	/// floating-point disparity image. The values of 8-bit / 16-bit signed formats are assumed to have no
	/// fractional bits. If the disparity is 16-bit signed format, as computed by [StereoBM] or
	/// [StereoSGBM] and maybe other algorithms, it should be divided by 16 (and scaled to float) before
	/// being used here.
	/// * _3dImage: Output 3-channel floating-point image of the same size as disparity. Each element of
	/// _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity map. If one
	/// uses Q obtained by [stereoRectify], then the returned points are represented in the first
	/// camera's rectified coordinate system.
	/// * Q: ![inline formula](https://latex.codecogs.com/png.latex?4%20%5Ctimes%204) perspective transformation matrix that can be obtained with
	/// [stereoRectify].
	/// * handleMissingValues: Indicates, whether the function should handle missing values (i.e.
	/// points where the disparity was not computed). If handleMissingValues=true, then pixels with the
	/// minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed
	/// to 3D points with a very large Z value (currently set to 10000).
	/// * ddepth: The optional output array depth. If it is -1, the output image will have CV_32F
	/// depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.
	///
	/// The function transforms a single-channel disparity map to a 3-channel image representing a 3D
	/// surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it
	/// computes:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0AX%20%5C%5C%0AY%20%5C%5C%0AZ%20%5C%5C%0AW%0A%5Cend%7Bbmatrix%7D%20%3D%20Q%20%5Cbegin%7Bbmatrix%7D%0Ax%20%5C%5C%0Ay%20%5C%5C%0A%5Ctexttt%7Bdisparity%7D%20%28x%2Cy%29%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%2E)
	/// ## See also
	/// To reproject a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform.
	///
	/// ## C++ default parameters
	/// * handle_missing_values: false
	/// * ddepth: -1
	#[inline]
	pub fn reproject_image_to_3d(disparity: &impl ToInputArray, _3d_image: &mut impl ToOutputArray, q: &impl ToInputArray, handle_missing_values: bool, ddepth: i32) -> Result<()> {
		input_array_arg!(disparity);
		output_array_arg!(_3d_image);
		input_array_arg!(q);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_reprojectImageTo3D_const__InputArrayR_const__OutputArrayR_const__InputArrayR_bool_int(disparity.as_raw__InputArray(), _3d_image.as_raw__OutputArray(), q.as_raw__InputArray(), handle_missing_values, ddepth, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Calculates the Sampson Distance between two points.
	///
	/// The function cv::sampsonDistance calculates and returns the first order approximation of the geometric error as:
	/// ![block formula](https://latex.codecogs.com/png.latex?%0Asd%28%20%5Ctexttt%7Bpt1%7D%20%2C%20%5Ctexttt%7Bpt2%7D%20%29%3D%0A%5Cfrac%7B%28%5Ctexttt%7Bpt2%7D%5Et%20%5Ccdot%20%5Ctexttt%7BF%7D%20%5Ccdot%20%5Ctexttt%7Bpt1%7D%29%5E2%7D%0A%7B%28%28%5Ctexttt%7BF%7D%20%5Ccdot%20%5Ctexttt%7Bpt1%7D%29%280%29%29%5E2%20%2B%0A%28%28%5Ctexttt%7BF%7D%20%5Ccdot%20%5Ctexttt%7Bpt1%7D%29%281%29%29%5E2%20%2B%0A%28%28%5Ctexttt%7BF%7D%5Et%20%5Ccdot%20%5Ctexttt%7Bpt2%7D%29%280%29%29%5E2%20%2B%0A%28%28%5Ctexttt%7BF%7D%5Et%20%5Ccdot%20%5Ctexttt%7Bpt2%7D%29%281%29%29%5E2%7D%0A)
	/// The fundamental matrix may be calculated using the [find_fundamental_mat] function. See [HartleyZ00](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_HartleyZ00) 11.4.3 for details.
	/// ## Parameters
	/// * pt1: first homogeneous 2d point
	/// * pt2: second homogeneous 2d point
	/// * F: fundamental matrix
	/// ## Returns
	/// The computed Sampson distance.
	#[inline]
	pub fn sampson_distance(pt1: &impl ToInputArray, pt2: &impl ToInputArray, f: &impl ToInputArray) -> Result<f64> {
		input_array_arg!(pt1);
		input_array_arg!(pt2);
		input_array_arg!(f);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_sampsonDistance_const__InputArrayR_const__InputArrayR_const__InputArrayR(pt1.as_raw__InputArray(), pt2.as_raw__InputArray(), f.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds an object pose ![inline formula](https://latex.codecogs.com/png.latex?%20%7B%7D%5E%7Bc%7D%5Cmathbf%7BT%7D%5Fo%20) from **3** 3D-2D point correspondences.
	///
	/// ![Perspective projection, from object to camera frame](https://docs.opencv.org/4.12.0/pinhole_homogeneous_transformation.png){ width=50% }
	/// ## See also
	/// [calib3d_solvePnP]
	///
	/// ## Parameters
	/// * objectPoints: Array of object points in the object coordinate space, 3x3 1-channel or
	/// 1x3/3x1 3-channel. vector\<Point3f\> can be also passed here.
	/// * imagePoints: Array of corresponding image points, 3x2 1-channel or 1x3/3x1 2-channel.
	///  vector\<Point2f\> can be also passed here.
	/// * cameraMatrix: Input camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs). If the vector is NULL/empty, the zero distortion coefficients are
	/// assumed.
	/// * rvecs: Output rotation vectors (see [Rodrigues] ) that, together with tvecs, brings points from
	/// the model coordinate system to the camera coordinate system. A P3P problem has up to 4 solutions.
	/// * tvecs: Output translation vectors.
	/// * flags: Method for solving a P3P problem:
	/// *   [SOLVEPNP_P3P] Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
	/// "Complete Solution Classification for the Perspective-Three-Point Problem" ([gao2003complete](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_gao2003complete)).
	/// *   [SOLVEPNP_AP3P] Method is based on the paper of T. Ke and S. Roumeliotis.
	/// "An Efficient Algebraic Solution to the Perspective-Three-Point Problem" ([Ke17](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Ke17)).
	///
	/// The function estimates the object pose given 3 object points, their corresponding image
	/// projections, as well as the camera intrinsic matrix and the distortion coefficients.
	///
	///
	/// Note:
	/// The solutions are sorted by reprojection errors (lowest to highest).
	#[inline]
	pub fn solve_p3p(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray, flags: i32) -> Result<i32> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_solveP3P_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds an object pose ![inline formula](https://latex.codecogs.com/png.latex?%20%7B%7D%5E%7Bc%7D%5Cmathbf%7BT%7D%5Fo%20) from 3D-2D point correspondences.
	///
	/// ![Perspective projection, from object to camera frame](https://docs.opencv.org/4.12.0/pinhole_homogeneous_transformation.png){ width=50% }
	/// ## See also
	/// [calib3d_solvePnP]
	///
	/// This function returns a list of all the possible solutions (a solution is a <rotation vector, translation vector>
	/// couple), depending on the number of input points and the chosen method:
	/// - P3P methods ([SOLVEPNP_P3P], [SOLVEPNP_AP3P]): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
	/// - [SOLVEPNP_IPPE] Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
	/// - [SOLVEPNP_IPPE_SQUARE] Special case suitable for marker pose estimation.
	/// Number of input points must be 4 and 2 solutions are returned. Object points must be defined in the following order:
	///   - point 0: [-squareLength / 2,  squareLength / 2, 0]
	///   - point 1: [ squareLength / 2,  squareLength / 2, 0]
	///   - point 2: [ squareLength / 2, -squareLength / 2, 0]
	///   - point 3: [-squareLength / 2, -squareLength / 2, 0]
	/// - for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
	/// Only 1 solution is returned.
	///
	/// ## Parameters
	/// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or
	/// 1xN/Nx1 3-channel, where N is the number of points. vector\<Point3d\> can be also passed here.
	/// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
	/// where N is the number of points. vector\<Point2d\> can be also passed here.
	/// * cameraMatrix: Input camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs). If the vector is NULL/empty, the zero distortion coefficients are
	/// assumed.
	/// * rvecs: Vector of output rotation vectors (see [Rodrigues] ) that, together with tvecs, brings points from
	/// the model coordinate system to the camera coordinate system.
	/// * tvecs: Vector of output translation vectors.
	/// * useExtrinsicGuess: Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
	/// the provided rvec and tvec values as initial approximations of the rotation and translation
	/// vectors, respectively, and further optimizes them.
	/// * flags: Method for solving a PnP problem: see [calib3d_solvePnP_flags]
	/// * rvec: Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is [SOLVEPNP_ITERATIVE]
	/// and useExtrinsicGuess is set to true.
	/// * tvec: Translation vector used to initialize an iterative PnP refinement algorithm, when flag is [SOLVEPNP_ITERATIVE]
	/// and useExtrinsicGuess is set to true.
	/// * reprojectionError: Optional vector of reprojection error, that is the RMS error
	/// (![inline formula](https://latex.codecogs.com/png.latex?%20%5Ctext%7BRMSE%7D%20%3D%20%5Csqrt%7B%5Cfrac%7B%5Csum%5F%7Bi%7D%5E%7BN%7D%20%5Cleft%20%28%20%5Chat%7By%5Fi%7D%20%2D%20y%5Fi%20%5Cright%20%29%5E2%7D%7BN%7D%7D%20)) between the input image points
	/// and the 3D object points projected with the estimated pose.
	///
	/// More information is described in [calib3d_solvePnP]
	///
	///
	/// Note:
	///    *   An example of how to use solvePnP for planar augmented reality can be found at
	///        opencv_source_code/samples/python/plane_ar.py
	///    *   If you are using Python:
	///        - Numpy array slices won't work as input because solvePnP requires contiguous
	///        arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of
	///        modules/calib3d/src/solvepnp.cpp version 2.4.9)
	///        - The P3P algorithm requires image points to be in an array of shape (N,1,2) due
	///        to its calling of [undistort_points] (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
	///        which requires 2-channel information.
	///        - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of
	///        it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints =
	///        np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
	///    *   The methods [SOLVEPNP_DLS] and [SOLVEPNP_UPNP] cannot be used as the current implementations are
	///        unstable and sometimes give completely wrong results. If you pass one of these two
	///        flags, [SOLVEPNP_EPNP] method will be used instead.
	///    *   The minimum number of points is 4 in the general case. In the case of [SOLVEPNP_P3P] and [SOLVEPNP_AP3P]
	///        methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions
	///        of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
	///    *   With [SOLVEPNP_ITERATIVE] method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points
	///        are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the
	///        global solution to converge.
	///    *   With [SOLVEPNP_IPPE] input points must be >= 4 and object points must be coplanar.
	///    *   With [SOLVEPNP_IPPE_SQUARE] this is a special case suitable for marker pose estimation.
	///        Number of input points must be 4. Object points must be defined in the following order:
	///          - point 0: [-squareLength / 2,  squareLength / 2, 0]
	///          - point 1: [ squareLength / 2,  squareLength / 2, 0]
	///          - point 2: [ squareLength / 2, -squareLength / 2, 0]
	///          - point 3: [-squareLength / 2, -squareLength / 2, 0]
	///    *   With [SOLVEPNP_SQPNP] input points must be >= 3
	///
	/// ## Note
	/// This alternative version of [solve_pnp_generic] function uses the following default values for its arguments:
	/// * use_extrinsic_guess: false
	/// * flags: SOLVEPNP_ITERATIVE
	/// * rvec: noArray()
	/// * tvec: noArray()
	/// * reprojection_error: noArray()
	#[inline]
	pub fn solve_pnp_generic_def(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray) -> Result<i32> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_solvePnPGeneric_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds an object pose ![inline formula](https://latex.codecogs.com/png.latex?%20%7B%7D%5E%7Bc%7D%5Cmathbf%7BT%7D%5Fo%20) from 3D-2D point correspondences.
	///
	/// ![Perspective projection, from object to camera frame](https://docs.opencv.org/4.12.0/pinhole_homogeneous_transformation.png){ width=50% }
	/// ## See also
	/// [calib3d_solvePnP]
	///
	/// This function returns a list of all the possible solutions (a solution is a <rotation vector, translation vector>
	/// couple), depending on the number of input points and the chosen method:
	/// - P3P methods ([SOLVEPNP_P3P], [SOLVEPNP_AP3P]): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
	/// - [SOLVEPNP_IPPE] Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
	/// - [SOLVEPNP_IPPE_SQUARE] Special case suitable for marker pose estimation.
	/// Number of input points must be 4 and 2 solutions are returned. Object points must be defined in the following order:
	///   - point 0: [-squareLength / 2,  squareLength / 2, 0]
	///   - point 1: [ squareLength / 2,  squareLength / 2, 0]
	///   - point 2: [ squareLength / 2, -squareLength / 2, 0]
	///   - point 3: [-squareLength / 2, -squareLength / 2, 0]
	/// - for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
	/// Only 1 solution is returned.
	///
	/// ## Parameters
	/// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or
	/// 1xN/Nx1 3-channel, where N is the number of points. vector\<Point3d\> can be also passed here.
	/// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
	/// where N is the number of points. vector\<Point2d\> can be also passed here.
	/// * cameraMatrix: Input camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs). If the vector is NULL/empty, the zero distortion coefficients are
	/// assumed.
	/// * rvecs: Vector of output rotation vectors (see [Rodrigues] ) that, together with tvecs, brings points from
	/// the model coordinate system to the camera coordinate system.
	/// * tvecs: Vector of output translation vectors.
	/// * useExtrinsicGuess: Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
	/// the provided rvec and tvec values as initial approximations of the rotation and translation
	/// vectors, respectively, and further optimizes them.
	/// * flags: Method for solving a PnP problem: see [calib3d_solvePnP_flags]
	/// * rvec: Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is [SOLVEPNP_ITERATIVE]
	/// and useExtrinsicGuess is set to true.
	/// * tvec: Translation vector used to initialize an iterative PnP refinement algorithm, when flag is [SOLVEPNP_ITERATIVE]
	/// and useExtrinsicGuess is set to true.
	/// * reprojectionError: Optional vector of reprojection error, that is the RMS error
	/// (![inline formula](https://latex.codecogs.com/png.latex?%20%5Ctext%7BRMSE%7D%20%3D%20%5Csqrt%7B%5Cfrac%7B%5Csum%5F%7Bi%7D%5E%7BN%7D%20%5Cleft%20%28%20%5Chat%7By%5Fi%7D%20%2D%20y%5Fi%20%5Cright%20%29%5E2%7D%7BN%7D%7D%20)) between the input image points
	/// and the 3D object points projected with the estimated pose.
	///
	/// More information is described in [calib3d_solvePnP]
	///
	///
	/// Note:
	///    *   An example of how to use solvePnP for planar augmented reality can be found at
	///        opencv_source_code/samples/python/plane_ar.py
	///    *   If you are using Python:
	///        - Numpy array slices won't work as input because solvePnP requires contiguous
	///        arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of
	///        modules/calib3d/src/solvepnp.cpp version 2.4.9)
	///        - The P3P algorithm requires image points to be in an array of shape (N,1,2) due
	///        to its calling of [undistort_points] (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
	///        which requires 2-channel information.
	///        - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of
	///        it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints =
	///        np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
	///    *   The methods [SOLVEPNP_DLS] and [SOLVEPNP_UPNP] cannot be used as the current implementations are
	///        unstable and sometimes give completely wrong results. If you pass one of these two
	///        flags, [SOLVEPNP_EPNP] method will be used instead.
	///    *   The minimum number of points is 4 in the general case. In the case of [SOLVEPNP_P3P] and [SOLVEPNP_AP3P]
	///        methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions
	///        of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
	///    *   With [SOLVEPNP_ITERATIVE] method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points
	///        are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the
	///        global solution to converge.
	///    *   With [SOLVEPNP_IPPE] input points must be >= 4 and object points must be coplanar.
	///    *   With [SOLVEPNP_IPPE_SQUARE] this is a special case suitable for marker pose estimation.
	///        Number of input points must be 4. Object points must be defined in the following order:
	///          - point 0: [-squareLength / 2,  squareLength / 2, 0]
	///          - point 1: [ squareLength / 2,  squareLength / 2, 0]
	///          - point 2: [ squareLength / 2, -squareLength / 2, 0]
	///          - point 3: [-squareLength / 2, -squareLength / 2, 0]
	///    *   With [SOLVEPNP_SQPNP] input points must be >= 3
	///
	/// ## C++ default parameters
	/// * use_extrinsic_guess: false
	/// * flags: SOLVEPNP_ITERATIVE
	/// * rvec: noArray()
	/// * tvec: noArray()
	/// * reprojection_error: noArray()
	#[inline]
	pub fn solve_pnp_generic(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray, use_extrinsic_guess: bool, flags: crate::calib3d::SolvePnPMethod, rvec: &impl ToInputArray, tvec: &impl ToInputArray, reprojection_error: &mut impl ToOutputArray) -> Result<i32> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		input_array_arg!(rvec);
		input_array_arg!(tvec);
		output_array_arg!(reprojection_error);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_solvePnPGeneric_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_bool_SolvePnPMethod_const__InputArrayR_const__InputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), use_extrinsic_guess, flags, rvec.as_raw__InputArray(), tvec.as_raw__InputArray(), reprojection_error.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds an object pose ![inline formula](https://latex.codecogs.com/png.latex?%20%7B%7D%5E%7Bc%7D%5Cmathbf%7BT%7D%5Fo%20) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.
	///
	/// ![Perspective projection, from object to camera frame](https://docs.opencv.org/4.12.0/pinhole_homogeneous_transformation.png){ width=50% }
	/// ## See also
	/// [calib3d_solvePnP]
	///
	/// ## Parameters
	/// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or
	/// 1xN/Nx1 3-channel, where N is the number of points. vector\<Point3d\> can be also passed here.
	/// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
	/// where N is the number of points. vector\<Point2d\> can be also passed here.
	/// * cameraMatrix: Input camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs). If the vector is NULL/empty, the zero distortion coefficients are
	/// assumed.
	/// * rvec: Output rotation vector (see [Rodrigues] ) that, together with tvec, brings points from
	/// the model coordinate system to the camera coordinate system.
	/// * tvec: Output translation vector.
	/// * useExtrinsicGuess: Parameter used for [SOLVEPNP_ITERATIVE]. If true (1), the function uses
	/// the provided rvec and tvec values as initial approximations of the rotation and translation
	/// vectors, respectively, and further optimizes them.
	/// * iterationsCount: Number of iterations.
	/// * reprojectionError: Inlier threshold value used by the RANSAC procedure. The parameter value
	/// is the maximum allowed distance between the observed and computed point projections to consider it
	/// an inlier.
	/// * confidence: The probability that the algorithm produces a useful result.
	/// * inliers: Output vector that contains indices of inliers in objectPoints and imagePoints .
	/// * flags: Method for solving a PnP problem (see [solvePnP] ).
	///
	/// The function estimates an object pose given a set of object points, their corresponding image
	/// projections, as well as the camera intrinsic matrix and the distortion coefficients. This function finds such
	/// a pose that minimizes reprojection error, that is, the sum of squared distances between the observed
	/// projections imagePoints and the projected (using [projectPoints] ) objectPoints. The use of RANSAC
	/// makes the function resistant to outliers.
	///
	///
	/// Note:
	///    *   An example of how to use solvePnPRansac for object detection can be found at
	///        [tutorial_real_time_pose]
	///    *   The default method used to estimate the camera pose for the Minimal Sample Sets step
	///        is #SOLVEPNP_EPNP. Exceptions are:
	///          - if you choose [SOLVEPNP_P3P] or #SOLVEPNP_AP3P, these methods will be used.
	///          - if the number of input points is equal to 4, [SOLVEPNP_P3P] is used.
	///    *   The method used to estimate the camera pose using all the inliers is defined by the
	///        flags parameters unless it is equal to [SOLVEPNP_P3P] or #SOLVEPNP_AP3P. In this case,
	///        the method [SOLVEPNP_EPNP] will be used instead.
	///
	/// ## Note
	/// This alternative version of [solve_pnp_ransac] function uses the following default values for its arguments:
	/// * use_extrinsic_guess: false
	/// * iterations_count: 100
	/// * reprojection_error: 8.0
	/// * confidence: 0.99
	/// * inliers: noArray()
	/// * flags: SOLVEPNP_ITERATIVE
	#[inline]
	pub fn solve_pnp_ransac_def(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvec: &mut impl ToOutputArray, tvec: &mut impl ToOutputArray) -> Result<bool> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(rvec);
		output_array_arg!(tvec);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_solvePnPRansac_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__OutputArray(), tvec.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds an object pose ![inline formula](https://latex.codecogs.com/png.latex?%20%7B%7D%5E%7Bc%7D%5Cmathbf%7BT%7D%5Fo%20) from 3D-2D point correspondences using the RANSAC scheme to deal with bad matches.
	///
	/// ![Perspective projection, from object to camera frame](https://docs.opencv.org/4.12.0/pinhole_homogeneous_transformation.png){ width=50% }
	/// ## See also
	/// [calib3d_solvePnP]
	///
	/// ## Parameters
	/// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or
	/// 1xN/Nx1 3-channel, where N is the number of points. vector\<Point3d\> can be also passed here.
	/// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
	/// where N is the number of points. vector\<Point2d\> can be also passed here.
	/// * cameraMatrix: Input camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs). If the vector is NULL/empty, the zero distortion coefficients are
	/// assumed.
	/// * rvec: Output rotation vector (see [Rodrigues] ) that, together with tvec, brings points from
	/// the model coordinate system to the camera coordinate system.
	/// * tvec: Output translation vector.
	/// * useExtrinsicGuess: Parameter used for [SOLVEPNP_ITERATIVE]. If true (1), the function uses
	/// the provided rvec and tvec values as initial approximations of the rotation and translation
	/// vectors, respectively, and further optimizes them.
	/// * iterationsCount: Number of iterations.
	/// * reprojectionError: Inlier threshold value used by the RANSAC procedure. The parameter value
	/// is the maximum allowed distance between the observed and computed point projections to consider it
	/// an inlier.
	/// * confidence: The probability that the algorithm produces a useful result.
	/// * inliers: Output vector that contains indices of inliers in objectPoints and imagePoints .
	/// * flags: Method for solving a PnP problem (see [solvePnP] ).
	///
	/// The function estimates an object pose given a set of object points, their corresponding image
	/// projections, as well as the camera intrinsic matrix and the distortion coefficients. This function finds such
	/// a pose that minimizes reprojection error, that is, the sum of squared distances between the observed
	/// projections imagePoints and the projected (using [projectPoints] ) objectPoints. The use of RANSAC
	/// makes the function resistant to outliers.
	///
	///
	/// Note:
	///    *   An example of how to use solvePnPRansac for object detection can be found at
	///        [tutorial_real_time_pose]
	///    *   The default method used to estimate the camera pose for the Minimal Sample Sets step
	///        is #SOLVEPNP_EPNP. Exceptions are:
	///          - if you choose [SOLVEPNP_P3P] or #SOLVEPNP_AP3P, these methods will be used.
	///          - if the number of input points is equal to 4, [SOLVEPNP_P3P] is used.
	///    *   The method used to estimate the camera pose using all the inliers is defined by the
	///        flags parameters unless it is equal to [SOLVEPNP_P3P] or #SOLVEPNP_AP3P. In this case,
	///        the method [SOLVEPNP_EPNP] will be used instead.
	///
	/// ## C++ default parameters
	/// * use_extrinsic_guess: false
	/// * iterations_count: 100
	/// * reprojection_error: 8.0
	/// * confidence: 0.99
	/// * inliers: noArray()
	/// * flags: SOLVEPNP_ITERATIVE
	#[inline]
	pub fn solve_pnp_ransac(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvec: &mut impl ToOutputArray, tvec: &mut impl ToOutputArray, use_extrinsic_guess: bool, iterations_count: i32, reprojection_error: f32, confidence: f64, inliers: &mut impl ToOutputArray, flags: i32) -> Result<bool> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(rvec);
		output_array_arg!(tvec);
		output_array_arg!(inliers);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_solvePnPRansac_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_bool_int_float_double_const__OutputArrayR_int(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__OutputArray(), tvec.as_raw__OutputArray(), use_extrinsic_guess, iterations_count, reprojection_error, confidence, inliers.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// ## Note
	/// This alternative version of [solve_pnp_ransac_1] function uses the following default values for its arguments:
	/// * params: UsacParams()
	#[inline]
	pub fn solve_pnp_ransac_1_def(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &mut impl ToInputOutputArray, dist_coeffs: &impl ToInputArray, rvec: &mut impl ToOutputArray, tvec: &mut impl ToOutputArray, inliers: &mut impl ToOutputArray) -> Result<bool> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_output_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(rvec);
		output_array_arg!(tvec);
		output_array_arg!(inliers);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_solvePnPRansac_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputOutputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__OutputArray(), tvec.as_raw__OutputArray(), inliers.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// ## C++ default parameters
	/// * params: UsacParams()
	#[inline]
	pub fn solve_pnp_ransac_1(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &mut impl ToInputOutputArray, dist_coeffs: &impl ToInputArray, rvec: &mut impl ToOutputArray, tvec: &mut impl ToOutputArray, inliers: &mut impl ToOutputArray, params: crate::calib3d::UsacParams) -> Result<bool> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_output_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(rvec);
		output_array_arg!(tvec);
		output_array_arg!(inliers);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_solvePnPRansac_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const_UsacParamsR(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputOutputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__OutputArray(), tvec.as_raw__OutputArray(), inliers.as_raw__OutputArray(), &params, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame
	/// to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
	/// ## See also
	/// [calib3d_solvePnP]
	///
	/// ## Parameters
	/// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel,
	/// where N is the number of points. vector\<Point3d\> can also be passed here.
	/// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
	/// where N is the number of points. vector\<Point2d\> can also be passed here.
	/// * cameraMatrix: Input camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs). If the vector is NULL/empty, the zero distortion coefficients are
	/// assumed.
	/// * rvec: Input/Output rotation vector (see [Rodrigues] ) that, together with tvec, brings points from
	/// the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
	/// * tvec: Input/Output translation vector. Input values are used as an initial solution.
	/// * criteria: Criteria when to stop the Levenberg-Marquard iterative algorithm.
	///
	/// The function refines the object pose given at least 3 object points, their corresponding image
	/// projections, an initial solution for the rotation and translation vector,
	/// as well as the camera intrinsic matrix and the distortion coefficients.
	/// The function minimizes the projection error with respect to the rotation and the translation vectors, according
	/// to a Levenberg-Marquardt iterative minimization [Madsen04](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Madsen04) [Eade13](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Eade13) process.
	///
	/// ## Note
	/// This alternative version of [solve_pnp_refine_lm] function uses the following default values for its arguments:
	/// * criteria: TermCriteria(TermCriteria::EPS+TermCriteria::COUNT,20,FLT_EPSILON)
	#[inline]
	pub fn solve_pnp_refine_lm_def(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvec: &mut impl ToInputOutputArray, tvec: &mut impl ToInputOutputArray) -> Result<()> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		input_output_array_arg!(rvec);
		input_output_array_arg!(tvec);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_solvePnPRefineLM_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputOutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__InputOutputArray(), tvec.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame
	/// to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
	/// ## See also
	/// [calib3d_solvePnP]
	///
	/// ## Parameters
	/// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel,
	/// where N is the number of points. vector\<Point3d\> can also be passed here.
	/// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
	/// where N is the number of points. vector\<Point2d\> can also be passed here.
	/// * cameraMatrix: Input camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs). If the vector is NULL/empty, the zero distortion coefficients are
	/// assumed.
	/// * rvec: Input/Output rotation vector (see [Rodrigues] ) that, together with tvec, brings points from
	/// the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
	/// * tvec: Input/Output translation vector. Input values are used as an initial solution.
	/// * criteria: Criteria when to stop the Levenberg-Marquard iterative algorithm.
	///
	/// The function refines the object pose given at least 3 object points, their corresponding image
	/// projections, an initial solution for the rotation and translation vector,
	/// as well as the camera intrinsic matrix and the distortion coefficients.
	/// The function minimizes the projection error with respect to the rotation and the translation vectors, according
	/// to a Levenberg-Marquardt iterative minimization [Madsen04](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Madsen04) [Eade13](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Eade13) process.
	///
	/// ## C++ default parameters
	/// * criteria: TermCriteria(TermCriteria::EPS+TermCriteria::COUNT,20,FLT_EPSILON)
	#[inline]
	pub fn solve_pnp_refine_lm(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvec: &mut impl ToInputOutputArray, tvec: &mut impl ToInputOutputArray, criteria: core::TermCriteria) -> Result<()> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		input_output_array_arg!(rvec);
		input_output_array_arg!(tvec);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_solvePnPRefineLM_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_TermCriteria(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__InputOutputArray(), tvec.as_raw__InputOutputArray(), &criteria, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame
	/// to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
	/// ## See also
	/// [calib3d_solvePnP]
	///
	/// ## Parameters
	/// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel,
	/// where N is the number of points. vector\<Point3d\> can also be passed here.
	/// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
	/// where N is the number of points. vector\<Point2d\> can also be passed here.
	/// * cameraMatrix: Input camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs). If the vector is NULL/empty, the zero distortion coefficients are
	/// assumed.
	/// * rvec: Input/Output rotation vector (see [Rodrigues] ) that, together with tvec, brings points from
	/// the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
	/// * tvec: Input/Output translation vector. Input values are used as an initial solution.
	/// * criteria: Criteria when to stop the Levenberg-Marquard iterative algorithm.
	/// * VVSlambda: Gain for the virtual visual servoing control law, equivalent to the ![inline formula](https://latex.codecogs.com/png.latex?%5Calpha)
	/// gain in the Damped Gauss-Newton formulation.
	///
	/// The function refines the object pose given at least 3 object points, their corresponding image
	/// projections, an initial solution for the rotation and translation vector,
	/// as well as the camera intrinsic matrix and the distortion coefficients.
	/// The function minimizes the projection error with respect to the rotation and the translation vectors, using a
	/// virtual visual servoing (VVS) [Chaumette06](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Chaumette06) [Marchand16](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Marchand16) scheme.
	///
	/// ## Note
	/// This alternative version of [solve_pnp_refine_vvs] function uses the following default values for its arguments:
	/// * criteria: TermCriteria(TermCriteria::EPS+TermCriteria::COUNT,20,FLT_EPSILON)
	/// * vv_slambda: 1
	#[inline]
	pub fn solve_pnp_refine_vvs_def(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvec: &mut impl ToInputOutputArray, tvec: &mut impl ToInputOutputArray) -> Result<()> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		input_output_array_arg!(rvec);
		input_output_array_arg!(tvec);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_solvePnPRefineVVS_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputOutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__InputOutputArray(), tvec.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame
	/// to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
	/// ## See also
	/// [calib3d_solvePnP]
	///
	/// ## Parameters
	/// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel,
	/// where N is the number of points. vector\<Point3d\> can also be passed here.
	/// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
	/// where N is the number of points. vector\<Point2d\> can also be passed here.
	/// * cameraMatrix: Input camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs). If the vector is NULL/empty, the zero distortion coefficients are
	/// assumed.
	/// * rvec: Input/Output rotation vector (see [Rodrigues] ) that, together with tvec, brings points from
	/// the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
	/// * tvec: Input/Output translation vector. Input values are used as an initial solution.
	/// * criteria: Criteria when to stop the Levenberg-Marquard iterative algorithm.
	/// * VVSlambda: Gain for the virtual visual servoing control law, equivalent to the ![inline formula](https://latex.codecogs.com/png.latex?%5Calpha)
	/// gain in the Damped Gauss-Newton formulation.
	///
	/// The function refines the object pose given at least 3 object points, their corresponding image
	/// projections, an initial solution for the rotation and translation vector,
	/// as well as the camera intrinsic matrix and the distortion coefficients.
	/// The function minimizes the projection error with respect to the rotation and the translation vectors, using a
	/// virtual visual servoing (VVS) [Chaumette06](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Chaumette06) [Marchand16](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Marchand16) scheme.
	///
	/// ## C++ default parameters
	/// * criteria: TermCriteria(TermCriteria::EPS+TermCriteria::COUNT,20,FLT_EPSILON)
	/// * vv_slambda: 1
	#[inline]
	pub fn solve_pnp_refine_vvs(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvec: &mut impl ToInputOutputArray, tvec: &mut impl ToInputOutputArray, criteria: core::TermCriteria, vv_slambda: f64) -> Result<()> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		input_output_array_arg!(rvec);
		input_output_array_arg!(tvec);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_solvePnPRefineVVS_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_TermCriteria_double(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__InputOutputArray(), tvec.as_raw__InputOutputArray(), &criteria, vv_slambda, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds an object pose ![inline formula](https://latex.codecogs.com/png.latex?%20%7B%7D%5E%7Bc%7D%5Cmathbf%7BT%7D%5Fo%20) from 3D-2D point correspondences:
	///
	/// ![Perspective projection, from object to camera frame](https://docs.opencv.org/4.12.0/pinhole_homogeneous_transformation.png){ width=50% }
	/// ## See also
	/// [calib3d_solvePnP]
	///
	/// This function returns the rotation and the translation vectors that transform a 3D point expressed in the object
	/// coordinate frame to the camera coordinate frame, using different methods:
	/// - P3P methods ([SOLVEPNP_P3P], [SOLVEPNP_AP3P]): need 4 input points to return a unique solution.
	/// - [SOLVEPNP_IPPE] Input points must be >= 4 and object points must be coplanar.
	/// - [SOLVEPNP_IPPE_SQUARE] Special case suitable for marker pose estimation.
	/// Number of input points must be 4. Object points must be defined in the following order:
	///   - point 0: [-squareLength / 2,  squareLength / 2, 0]
	///   - point 1: [ squareLength / 2,  squareLength / 2, 0]
	///   - point 2: [ squareLength / 2, -squareLength / 2, 0]
	///   - point 3: [-squareLength / 2, -squareLength / 2, 0]
	/// - for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
	///
	/// ## Parameters
	/// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or
	/// 1xN/Nx1 3-channel, where N is the number of points. vector\<Point3d\> can be also passed here.
	/// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
	/// where N is the number of points. vector\<Point2d\> can be also passed here.
	/// * cameraMatrix: Input camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs). If the vector is NULL/empty, the zero distortion coefficients are
	/// assumed.
	/// * rvec: Output rotation vector (see [Rodrigues] ) that, together with tvec, brings points from
	/// the model coordinate system to the camera coordinate system.
	/// * tvec: Output translation vector.
	/// * useExtrinsicGuess: Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
	/// the provided rvec and tvec values as initial approximations of the rotation and translation
	/// vectors, respectively, and further optimizes them.
	/// * flags: Method for solving a PnP problem: see [calib3d_solvePnP_flags]
	///
	/// More information about Perspective-n-Points is described in [calib3d_solvePnP]
	///
	///
	/// Note:
	///    *   An example of how to use solvePnP for planar augmented reality can be found at
	///        opencv_source_code/samples/python/plane_ar.py
	///    *   If you are using Python:
	///        - Numpy array slices won't work as input because solvePnP requires contiguous
	///        arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of
	///        modules/calib3d/src/solvepnp.cpp version 2.4.9)
	///        - The P3P algorithm requires image points to be in an array of shape (N,1,2) due
	///        to its calling of [undistort_points] (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
	///        which requires 2-channel information.
	///        - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of
	///        it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints =
	///        np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
	///    *   The methods [SOLVEPNP_DLS] and [SOLVEPNP_UPNP] cannot be used as the current implementations are
	///        unstable and sometimes give completely wrong results. If you pass one of these two
	///        flags, [SOLVEPNP_EPNP] method will be used instead.
	///    *   The minimum number of points is 4 in the general case. In the case of [SOLVEPNP_P3P] and [SOLVEPNP_AP3P]
	///        methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions
	///        of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
	///    *   With [SOLVEPNP_ITERATIVE] method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points
	///        are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the
	///        global solution to converge.
	///    *   With [SOLVEPNP_IPPE] input points must be >= 4 and object points must be coplanar.
	///    *   With [SOLVEPNP_IPPE_SQUARE] this is a special case suitable for marker pose estimation.
	///        Number of input points must be 4. Object points must be defined in the following order:
	///          - point 0: [-squareLength / 2,  squareLength / 2, 0]
	///          - point 1: [ squareLength / 2,  squareLength / 2, 0]
	///          - point 2: [ squareLength / 2, -squareLength / 2, 0]
	///          - point 3: [-squareLength / 2, -squareLength / 2, 0]
	///    *   With [SOLVEPNP_SQPNP] input points must be >= 3
	///
	/// ## Note
	/// This alternative version of [solve_pnp] function uses the following default values for its arguments:
	/// * use_extrinsic_guess: false
	/// * flags: SOLVEPNP_ITERATIVE
	#[inline]
	pub fn solve_pnp_def(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvec: &mut impl ToOutputArray, tvec: &mut impl ToOutputArray) -> Result<bool> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(rvec);
		output_array_arg!(tvec);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_solvePnP_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__OutputArray(), tvec.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Finds an object pose ![inline formula](https://latex.codecogs.com/png.latex?%20%7B%7D%5E%7Bc%7D%5Cmathbf%7BT%7D%5Fo%20) from 3D-2D point correspondences:
	///
	/// ![Perspective projection, from object to camera frame](https://docs.opencv.org/4.12.0/pinhole_homogeneous_transformation.png){ width=50% }
	/// ## See also
	/// [calib3d_solvePnP]
	///
	/// This function returns the rotation and the translation vectors that transform a 3D point expressed in the object
	/// coordinate frame to the camera coordinate frame, using different methods:
	/// - P3P methods ([SOLVEPNP_P3P], [SOLVEPNP_AP3P]): need 4 input points to return a unique solution.
	/// - [SOLVEPNP_IPPE] Input points must be >= 4 and object points must be coplanar.
	/// - [SOLVEPNP_IPPE_SQUARE] Special case suitable for marker pose estimation.
	/// Number of input points must be 4. Object points must be defined in the following order:
	///   - point 0: [-squareLength / 2,  squareLength / 2, 0]
	///   - point 1: [ squareLength / 2,  squareLength / 2, 0]
	///   - point 2: [ squareLength / 2, -squareLength / 2, 0]
	///   - point 3: [-squareLength / 2, -squareLength / 2, 0]
	/// - for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
	///
	/// ## Parameters
	/// * objectPoints: Array of object points in the object coordinate space, Nx3 1-channel or
	/// 1xN/Nx1 3-channel, where N is the number of points. vector\<Point3d\> can be also passed here.
	/// * imagePoints: Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
	/// where N is the number of points. vector\<Point2d\> can be also passed here.
	/// * cameraMatrix: Input camera intrinsic matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Ccameramatrix%7BA%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%5Cdistcoeffs). If the vector is NULL/empty, the zero distortion coefficients are
	/// assumed.
	/// * rvec: Output rotation vector (see [Rodrigues] ) that, together with tvec, brings points from
	/// the model coordinate system to the camera coordinate system.
	/// * tvec: Output translation vector.
	/// * useExtrinsicGuess: Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
	/// the provided rvec and tvec values as initial approximations of the rotation and translation
	/// vectors, respectively, and further optimizes them.
	/// * flags: Method for solving a PnP problem: see [calib3d_solvePnP_flags]
	///
	/// More information about Perspective-n-Points is described in [calib3d_solvePnP]
	///
	///
	/// Note:
	///    *   An example of how to use solvePnP for planar augmented reality can be found at
	///        opencv_source_code/samples/python/plane_ar.py
	///    *   If you are using Python:
	///        - Numpy array slices won't work as input because solvePnP requires contiguous
	///        arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of
	///        modules/calib3d/src/solvepnp.cpp version 2.4.9)
	///        - The P3P algorithm requires image points to be in an array of shape (N,1,2) due
	///        to its calling of [undistort_points] (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
	///        which requires 2-channel information.
	///        - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of
	///        it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints =
	///        np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
	///    *   The methods [SOLVEPNP_DLS] and [SOLVEPNP_UPNP] cannot be used as the current implementations are
	///        unstable and sometimes give completely wrong results. If you pass one of these two
	///        flags, [SOLVEPNP_EPNP] method will be used instead.
	///    *   The minimum number of points is 4 in the general case. In the case of [SOLVEPNP_P3P] and [SOLVEPNP_AP3P]
	///        methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions
	///        of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
	///    *   With [SOLVEPNP_ITERATIVE] method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points
	///        are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the
	///        global solution to converge.
	///    *   With [SOLVEPNP_IPPE] input points must be >= 4 and object points must be coplanar.
	///    *   With [SOLVEPNP_IPPE_SQUARE] this is a special case suitable for marker pose estimation.
	///        Number of input points must be 4. Object points must be defined in the following order:
	///          - point 0: [-squareLength / 2,  squareLength / 2, 0]
	///          - point 1: [ squareLength / 2,  squareLength / 2, 0]
	///          - point 2: [ squareLength / 2, -squareLength / 2, 0]
	///          - point 3: [-squareLength / 2, -squareLength / 2, 0]
	///    *   With [SOLVEPNP_SQPNP] input points must be >= 3
	///
	/// ## C++ default parameters
	/// * use_extrinsic_guess: false
	/// * flags: SOLVEPNP_ITERATIVE
	#[inline]
	pub fn solve_pnp(object_points: &impl ToInputArray, image_points: &impl ToInputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, rvec: &mut impl ToOutputArray, tvec: &mut impl ToOutputArray, use_extrinsic_guess: bool, flags: i32) -> Result<bool> {
		input_array_arg!(object_points);
		input_array_arg!(image_points);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		output_array_arg!(rvec);
		output_array_arg!(tvec);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_solvePnP_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_bool_int(object_points.as_raw__InputArray(), image_points.as_raw__InputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), rvec.as_raw__OutputArray(), tvec.as_raw__OutputArray(), use_extrinsic_guess, flags, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Calibrates a stereo camera set up. This function finds the intrinsic parameters
	/// for each of the two cameras and the extrinsic parameters between the two cameras.
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of the calibration pattern points. The same structure as
	/// in [calibrateCamera]. For each pattern view, both cameras need to see the same object
	/// points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be
	/// equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to
	/// be equal for each i.
	/// * imagePoints1: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the first camera. The same structure as in [calibrateCamera].
	/// * imagePoints2: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the second camera. The same structure as in [calibrateCamera].
	/// * cameraMatrix1: Input/output camera intrinsic matrix for the first camera, the same as in
	/// [calibrateCamera]. Furthermore, for the stereo case, additional flags may be used, see below.
	/// * distCoeffs1: Input/output vector of distortion coefficients, the same as in
	/// [calibrateCamera].
	/// * cameraMatrix2: Input/output second camera intrinsic matrix for the second camera. See description for
	/// cameraMatrix1.
	/// * distCoeffs2: Input/output lens distortion coefficients for the second camera. See
	/// description for distCoeffs1.
	/// * imageSize: Size of the image used only to initialize the camera intrinsic matrices.
	/// * R: Output rotation matrix. Together with the translation vector T, this matrix brings
	/// points given in the first camera's coordinate system to points in the second camera's
	/// coordinate system. In more technical terms, the tuple of R and T performs a change of basis
	/// from the first camera's coordinate system to the second camera's coordinate system. Due to its
	/// duality, this tuple is equivalent to the position of the first camera with respect to the
	/// second camera coordinate system.
	/// * T: Output translation vector, see description above.
	/// * E: Output essential matrix.
	/// * F: Output fundamental matrix.
	/// * rvecs: Output vector of rotation vectors ( [Rodrigues] ) estimated for each pattern view in the
	/// coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
	/// i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
	/// description) brings the calibration pattern from the object coordinate space (in which object points are
	/// specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
	/// the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
	/// to camera coordinate space of the first camera of the stereo pair.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view, see parameter description
	/// of previous output parameter ( rvecs ).
	/// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of the following values:
	/// *   [CALIB_FIX_INTRINSIC] Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F
	/// matrices are estimated.
	/// *   [CALIB_USE_INTRINSIC_GUESS] Optimize some or all of the intrinsic parameters
	/// according to the specified flags. Initial values are provided by the user.
	/// *   [CALIB_USE_EXTRINSIC_GUESS] R and T contain valid initial values that are optimized further.
	/// Otherwise R and T are initialized to the median value of the pattern views (each dimension separately).
	/// *   [CALIB_FIX_PRINCIPAL_POINT] Fix the principal points during the optimization.
	/// *   [CALIB_FIX_FOCAL_LENGTH] Fix ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fy) .
	/// *   [CALIB_FIX_ASPECT_RATIO] Optimize ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fy) . Fix the ratio ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fx%2Ff%5E%7B%28j%29%7D%5Fy)
	/// .
	/// *   [CALIB_SAME_FOCAL_LENGTH] Enforce ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%280%29%7D%5Fx%3Df%5E%7B%281%29%7D%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%280%29%7D%5Fy%3Df%5E%7B%281%29%7D%5Fy) .
	/// *   [CALIB_ZERO_TANGENT_DIST] Set tangential distortion coefficients for each camera to
	/// zeros and fix there.
	/// *   [CALIB_FIX_K1],..., [CALIB_FIX_K6] Do not change the corresponding radial
	/// distortion coefficient during the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set,
	/// the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_RATIONAL_MODEL] Enable coefficients k4, k5, and k6. To provide the backward
	/// compatibility, this extra flag should be explicitly specified to make the calibration
	/// function use the rational model and return 8 coefficients. If the flag is not set, the
	/// function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_THIN_PRISM_MODEL] Coefficients s1, s2, s3 and s4 are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the thin prism model and return 12 coefficients. If the flag is not
	/// set, the function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_FIX_S1_S2_S3_S4] The thin prism distortion coefficients are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_TILTED_MODEL] Coefficients tauX and tauY are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the tilted sensor model and return 14 coefficients. If the flag is not
	/// set, the function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_FIX_TAUX_TAUY] The coefficients of the tilted sensor model are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// The function estimates the transformation between two cameras making a stereo pair. If one computes
	/// the poses of an object relative to the first camera and to the second camera,
	/// ( ![inline formula](https://latex.codecogs.com/png.latex?R%5F1),![inline formula](https://latex.codecogs.com/png.latex?T%5F1) ) and (![inline formula](https://latex.codecogs.com/png.latex?R%5F2),![inline formula](https://latex.codecogs.com/png.latex?T%5F2)), respectively, for a stereo camera where the
	/// relative position and orientation between the two cameras are fixed, then those poses definitely
	/// relate to each other. This means, if the relative position and orientation (![inline formula](https://latex.codecogs.com/png.latex?R),![inline formula](https://latex.codecogs.com/png.latex?T)) of the
	/// two cameras is known, it is possible to compute (![inline formula](https://latex.codecogs.com/png.latex?R%5F2),![inline formula](https://latex.codecogs.com/png.latex?T%5F2)) when (![inline formula](https://latex.codecogs.com/png.latex?R%5F1),![inline formula](https://latex.codecogs.com/png.latex?T%5F1)) is
	/// given. This is what the described function does. It computes (![inline formula](https://latex.codecogs.com/png.latex?R),![inline formula](https://latex.codecogs.com/png.latex?T)) such that:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?R%5F2%3DR%20R%5F1)
	/// ![block formula](https://latex.codecogs.com/png.latex?T%5F2%3DR%20T%5F1%20%2B%20T%2E)
	///
	/// Therefore, one can compute the coordinate representation of a 3D point for the second camera's
	/// coordinate system when given the point's coordinate representation in the first camera's coordinate
	/// system:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0AX%5F2%20%5C%5C%0AY%5F2%20%5C%5C%0AZ%5F2%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0AR%20%26%20T%20%5C%5C%0A0%20%26%201%0A%5Cend%7Bbmatrix%7D%20%5Cbegin%7Bbmatrix%7D%0AX%5F1%20%5C%5C%0AY%5F1%20%5C%5C%0AZ%5F1%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%2E)
	///
	///
	/// Optionally, it computes the essential matrix E:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?E%3D%20%5Cbegin%7Bbmatrix%7D%200%20%26%20%2DT%5F2%20%26%20T%5F1%5C%5C%20T%5F2%20%26%200%20%26%20%2DT%5F0%5C%5C%20%2DT%5F1%20%26%20T%5F0%20%26%200%20%5Cend%7Bbmatrix%7D%20R)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?T%5Fi) are components of the translation vector ![inline formula](https://latex.codecogs.com/png.latex?T) : ![inline formula](https://latex.codecogs.com/png.latex?T%3D%5BT%5F0%2C%20T%5F1%2C%20T%5F2%5D%5ET) .
	/// And the function can also compute the fundamental matrix F:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?F%20%3D%20cameraMatrix2%5E%7B%2DT%7D%5Ccdot%20E%20%5Ccdot%20cameraMatrix1%5E%7B%2D1%7D)
	///
	/// Besides the stereo-related information, the function can also perform a full calibration of each of
	/// the two cameras. However, due to the high dimensionality of the parameter space and noise in the
	/// input data, the function can diverge from the correct solution. If the intrinsic parameters can be
	/// estimated with high accuracy for each of the cameras individually (for example, using
	/// [calibrate_camera] ), you are recommended to do so and then pass [CALIB_FIX_INTRINSIC] flag to the
	/// function along with the computed intrinsic parameters. Otherwise, if all the parameters are
	/// estimated at once, it makes sense to restrict some parameters, for example, pass
	///  [CALIB_SAME_FOCAL_LENGTH] and [CALIB_ZERO_TANGENT_DIST] flags, which is usually a
	/// reasonable assumption.
	///
	/// Similarly to #calibrateCamera, the function minimizes the total re-projection error for all the
	/// points in all the available views from both cameras. The function returns the final value of the
	/// re-projection error.
	///
	/// ## Overloaded parameters
	///
	///
	/// ## Note
	/// This alternative version of [stereo_calibrate_1] function uses the following default values for its arguments:
	/// * flags: CALIB_FIX_INTRINSIC
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,30,1e-6)
	#[inline]
	pub fn stereo_calibrate_1_def(object_points: &impl ToInputArray, image_points1: &impl ToInputArray, image_points2: &impl ToInputArray, camera_matrix1: &mut impl ToInputOutputArray, dist_coeffs1: &mut impl ToInputOutputArray, camera_matrix2: &mut impl ToInputOutputArray, dist_coeffs2: &mut impl ToInputOutputArray, image_size: core::Size, r: &mut impl ToInputOutputArray, t: &mut impl ToInputOutputArray, e: &mut impl ToOutputArray, f: &mut impl ToOutputArray, per_view_errors: &mut impl ToOutputArray) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points1);
		input_array_arg!(image_points2);
		input_output_array_arg!(camera_matrix1);
		input_output_array_arg!(dist_coeffs1);
		input_output_array_arg!(camera_matrix2);
		input_output_array_arg!(dist_coeffs2);
		input_output_array_arg!(r);
		input_output_array_arg!(t);
		output_array_arg!(e);
		output_array_arg!(f);
		output_array_arg!(per_view_errors);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_stereoCalibrate_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_Size_const__InputOutputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points1.as_raw__InputArray(), image_points2.as_raw__InputArray(), camera_matrix1.as_raw__InputOutputArray(), dist_coeffs1.as_raw__InputOutputArray(), camera_matrix2.as_raw__InputOutputArray(), dist_coeffs2.as_raw__InputOutputArray(), &image_size, r.as_raw__InputOutputArray(), t.as_raw__InputOutputArray(), e.as_raw__OutputArray(), f.as_raw__OutputArray(), per_view_errors.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Calibrates a stereo camera set up. This function finds the intrinsic parameters
	/// for each of the two cameras and the extrinsic parameters between the two cameras.
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of the calibration pattern points. The same structure as
	/// in [calibrateCamera]. For each pattern view, both cameras need to see the same object
	/// points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be
	/// equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to
	/// be equal for each i.
	/// * imagePoints1: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the first camera. The same structure as in [calibrateCamera].
	/// * imagePoints2: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the second camera. The same structure as in [calibrateCamera].
	/// * cameraMatrix1: Input/output camera intrinsic matrix for the first camera, the same as in
	/// [calibrateCamera]. Furthermore, for the stereo case, additional flags may be used, see below.
	/// * distCoeffs1: Input/output vector of distortion coefficients, the same as in
	/// [calibrateCamera].
	/// * cameraMatrix2: Input/output second camera intrinsic matrix for the second camera. See description for
	/// cameraMatrix1.
	/// * distCoeffs2: Input/output lens distortion coefficients for the second camera. See
	/// description for distCoeffs1.
	/// * imageSize: Size of the image used only to initialize the camera intrinsic matrices.
	/// * R: Output rotation matrix. Together with the translation vector T, this matrix brings
	/// points given in the first camera's coordinate system to points in the second camera's
	/// coordinate system. In more technical terms, the tuple of R and T performs a change of basis
	/// from the first camera's coordinate system to the second camera's coordinate system. Due to its
	/// duality, this tuple is equivalent to the position of the first camera with respect to the
	/// second camera coordinate system.
	/// * T: Output translation vector, see description above.
	/// * E: Output essential matrix.
	/// * F: Output fundamental matrix.
	/// * rvecs: Output vector of rotation vectors ( [Rodrigues] ) estimated for each pattern view in the
	/// coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
	/// i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
	/// description) brings the calibration pattern from the object coordinate space (in which object points are
	/// specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
	/// the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
	/// to camera coordinate space of the first camera of the stereo pair.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view, see parameter description
	/// of previous output parameter ( rvecs ).
	/// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of the following values:
	/// *   [CALIB_FIX_INTRINSIC] Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F
	/// matrices are estimated.
	/// *   [CALIB_USE_INTRINSIC_GUESS] Optimize some or all of the intrinsic parameters
	/// according to the specified flags. Initial values are provided by the user.
	/// *   [CALIB_USE_EXTRINSIC_GUESS] R and T contain valid initial values that are optimized further.
	/// Otherwise R and T are initialized to the median value of the pattern views (each dimension separately).
	/// *   [CALIB_FIX_PRINCIPAL_POINT] Fix the principal points during the optimization.
	/// *   [CALIB_FIX_FOCAL_LENGTH] Fix ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fy) .
	/// *   [CALIB_FIX_ASPECT_RATIO] Optimize ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fy) . Fix the ratio ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fx%2Ff%5E%7B%28j%29%7D%5Fy)
	/// .
	/// *   [CALIB_SAME_FOCAL_LENGTH] Enforce ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%280%29%7D%5Fx%3Df%5E%7B%281%29%7D%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%280%29%7D%5Fy%3Df%5E%7B%281%29%7D%5Fy) .
	/// *   [CALIB_ZERO_TANGENT_DIST] Set tangential distortion coefficients for each camera to
	/// zeros and fix there.
	/// *   [CALIB_FIX_K1],..., [CALIB_FIX_K6] Do not change the corresponding radial
	/// distortion coefficient during the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set,
	/// the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_RATIONAL_MODEL] Enable coefficients k4, k5, and k6. To provide the backward
	/// compatibility, this extra flag should be explicitly specified to make the calibration
	/// function use the rational model and return 8 coefficients. If the flag is not set, the
	/// function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_THIN_PRISM_MODEL] Coefficients s1, s2, s3 and s4 are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the thin prism model and return 12 coefficients. If the flag is not
	/// set, the function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_FIX_S1_S2_S3_S4] The thin prism distortion coefficients are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_TILTED_MODEL] Coefficients tauX and tauY are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the tilted sensor model and return 14 coefficients. If the flag is not
	/// set, the function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_FIX_TAUX_TAUY] The coefficients of the tilted sensor model are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// The function estimates the transformation between two cameras making a stereo pair. If one computes
	/// the poses of an object relative to the first camera and to the second camera,
	/// ( ![inline formula](https://latex.codecogs.com/png.latex?R%5F1),![inline formula](https://latex.codecogs.com/png.latex?T%5F1) ) and (![inline formula](https://latex.codecogs.com/png.latex?R%5F2),![inline formula](https://latex.codecogs.com/png.latex?T%5F2)), respectively, for a stereo camera where the
	/// relative position and orientation between the two cameras are fixed, then those poses definitely
	/// relate to each other. This means, if the relative position and orientation (![inline formula](https://latex.codecogs.com/png.latex?R),![inline formula](https://latex.codecogs.com/png.latex?T)) of the
	/// two cameras is known, it is possible to compute (![inline formula](https://latex.codecogs.com/png.latex?R%5F2),![inline formula](https://latex.codecogs.com/png.latex?T%5F2)) when (![inline formula](https://latex.codecogs.com/png.latex?R%5F1),![inline formula](https://latex.codecogs.com/png.latex?T%5F1)) is
	/// given. This is what the described function does. It computes (![inline formula](https://latex.codecogs.com/png.latex?R),![inline formula](https://latex.codecogs.com/png.latex?T)) such that:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?R%5F2%3DR%20R%5F1)
	/// ![block formula](https://latex.codecogs.com/png.latex?T%5F2%3DR%20T%5F1%20%2B%20T%2E)
	///
	/// Therefore, one can compute the coordinate representation of a 3D point for the second camera's
	/// coordinate system when given the point's coordinate representation in the first camera's coordinate
	/// system:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0AX%5F2%20%5C%5C%0AY%5F2%20%5C%5C%0AZ%5F2%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0AR%20%26%20T%20%5C%5C%0A0%20%26%201%0A%5Cend%7Bbmatrix%7D%20%5Cbegin%7Bbmatrix%7D%0AX%5F1%20%5C%5C%0AY%5F1%20%5C%5C%0AZ%5F1%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%2E)
	///
	///
	/// Optionally, it computes the essential matrix E:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?E%3D%20%5Cbegin%7Bbmatrix%7D%200%20%26%20%2DT%5F2%20%26%20T%5F1%5C%5C%20T%5F2%20%26%200%20%26%20%2DT%5F0%5C%5C%20%2DT%5F1%20%26%20T%5F0%20%26%200%20%5Cend%7Bbmatrix%7D%20R)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?T%5Fi) are components of the translation vector ![inline formula](https://latex.codecogs.com/png.latex?T) : ![inline formula](https://latex.codecogs.com/png.latex?T%3D%5BT%5F0%2C%20T%5F1%2C%20T%5F2%5D%5ET) .
	/// And the function can also compute the fundamental matrix F:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?F%20%3D%20cameraMatrix2%5E%7B%2DT%7D%5Ccdot%20E%20%5Ccdot%20cameraMatrix1%5E%7B%2D1%7D)
	///
	/// Besides the stereo-related information, the function can also perform a full calibration of each of
	/// the two cameras. However, due to the high dimensionality of the parameter space and noise in the
	/// input data, the function can diverge from the correct solution. If the intrinsic parameters can be
	/// estimated with high accuracy for each of the cameras individually (for example, using
	/// [calibrate_camera] ), you are recommended to do so and then pass [CALIB_FIX_INTRINSIC] flag to the
	/// function along with the computed intrinsic parameters. Otherwise, if all the parameters are
	/// estimated at once, it makes sense to restrict some parameters, for example, pass
	///  [CALIB_SAME_FOCAL_LENGTH] and [CALIB_ZERO_TANGENT_DIST] flags, which is usually a
	/// reasonable assumption.
	///
	/// Similarly to #calibrateCamera, the function minimizes the total re-projection error for all the
	/// points in all the available views from both cameras. The function returns the final value of the
	/// re-projection error.
	///
	/// ## Note
	/// This alternative version of [stereo_calibrate_extended] function uses the following default values for its arguments:
	/// * flags: CALIB_FIX_INTRINSIC
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,30,1e-6)
	#[inline]
	pub fn stereo_calibrate_extended_def(object_points: &impl ToInputArray, image_points1: &impl ToInputArray, image_points2: &impl ToInputArray, camera_matrix1: &mut impl ToInputOutputArray, dist_coeffs1: &mut impl ToInputOutputArray, camera_matrix2: &mut impl ToInputOutputArray, dist_coeffs2: &mut impl ToInputOutputArray, image_size: core::Size, r: &mut impl ToInputOutputArray, t: &mut impl ToInputOutputArray, e: &mut impl ToOutputArray, f: &mut impl ToOutputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray, per_view_errors: &mut impl ToOutputArray) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points1);
		input_array_arg!(image_points2);
		input_output_array_arg!(camera_matrix1);
		input_output_array_arg!(dist_coeffs1);
		input_output_array_arg!(camera_matrix2);
		input_output_array_arg!(dist_coeffs2);
		input_output_array_arg!(r);
		input_output_array_arg!(t);
		output_array_arg!(e);
		output_array_arg!(f);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		output_array_arg!(per_view_errors);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_stereoCalibrate_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_Size_const__InputOutputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points1.as_raw__InputArray(), image_points2.as_raw__InputArray(), camera_matrix1.as_raw__InputOutputArray(), dist_coeffs1.as_raw__InputOutputArray(), camera_matrix2.as_raw__InputOutputArray(), dist_coeffs2.as_raw__InputOutputArray(), &image_size, r.as_raw__InputOutputArray(), t.as_raw__InputOutputArray(), e.as_raw__OutputArray(), f.as_raw__OutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), per_view_errors.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Calibrates a stereo camera set up. This function finds the intrinsic parameters
	/// for each of the two cameras and the extrinsic parameters between the two cameras.
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of the calibration pattern points. The same structure as
	/// in [calibrateCamera]. For each pattern view, both cameras need to see the same object
	/// points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be
	/// equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to
	/// be equal for each i.
	/// * imagePoints1: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the first camera. The same structure as in [calibrateCamera].
	/// * imagePoints2: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the second camera. The same structure as in [calibrateCamera].
	/// * cameraMatrix1: Input/output camera intrinsic matrix for the first camera, the same as in
	/// [calibrateCamera]. Furthermore, for the stereo case, additional flags may be used, see below.
	/// * distCoeffs1: Input/output vector of distortion coefficients, the same as in
	/// [calibrateCamera].
	/// * cameraMatrix2: Input/output second camera intrinsic matrix for the second camera. See description for
	/// cameraMatrix1.
	/// * distCoeffs2: Input/output lens distortion coefficients for the second camera. See
	/// description for distCoeffs1.
	/// * imageSize: Size of the image used only to initialize the camera intrinsic matrices.
	/// * R: Output rotation matrix. Together with the translation vector T, this matrix brings
	/// points given in the first camera's coordinate system to points in the second camera's
	/// coordinate system. In more technical terms, the tuple of R and T performs a change of basis
	/// from the first camera's coordinate system to the second camera's coordinate system. Due to its
	/// duality, this tuple is equivalent to the position of the first camera with respect to the
	/// second camera coordinate system.
	/// * T: Output translation vector, see description above.
	/// * E: Output essential matrix.
	/// * F: Output fundamental matrix.
	/// * rvecs: Output vector of rotation vectors ( [Rodrigues] ) estimated for each pattern view in the
	/// coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
	/// i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
	/// description) brings the calibration pattern from the object coordinate space (in which object points are
	/// specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
	/// the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
	/// to camera coordinate space of the first camera of the stereo pair.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view, see parameter description
	/// of previous output parameter ( rvecs ).
	/// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of the following values:
	/// *   [CALIB_FIX_INTRINSIC] Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F
	/// matrices are estimated.
	/// *   [CALIB_USE_INTRINSIC_GUESS] Optimize some or all of the intrinsic parameters
	/// according to the specified flags. Initial values are provided by the user.
	/// *   [CALIB_USE_EXTRINSIC_GUESS] R and T contain valid initial values that are optimized further.
	/// Otherwise R and T are initialized to the median value of the pattern views (each dimension separately).
	/// *   [CALIB_FIX_PRINCIPAL_POINT] Fix the principal points during the optimization.
	/// *   [CALIB_FIX_FOCAL_LENGTH] Fix ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fy) .
	/// *   [CALIB_FIX_ASPECT_RATIO] Optimize ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fy) . Fix the ratio ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fx%2Ff%5E%7B%28j%29%7D%5Fy)
	/// .
	/// *   [CALIB_SAME_FOCAL_LENGTH] Enforce ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%280%29%7D%5Fx%3Df%5E%7B%281%29%7D%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%280%29%7D%5Fy%3Df%5E%7B%281%29%7D%5Fy) .
	/// *   [CALIB_ZERO_TANGENT_DIST] Set tangential distortion coefficients for each camera to
	/// zeros and fix there.
	/// *   [CALIB_FIX_K1],..., [CALIB_FIX_K6] Do not change the corresponding radial
	/// distortion coefficient during the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set,
	/// the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_RATIONAL_MODEL] Enable coefficients k4, k5, and k6. To provide the backward
	/// compatibility, this extra flag should be explicitly specified to make the calibration
	/// function use the rational model and return 8 coefficients. If the flag is not set, the
	/// function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_THIN_PRISM_MODEL] Coefficients s1, s2, s3 and s4 are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the thin prism model and return 12 coefficients. If the flag is not
	/// set, the function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_FIX_S1_S2_S3_S4] The thin prism distortion coefficients are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_TILTED_MODEL] Coefficients tauX and tauY are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the tilted sensor model and return 14 coefficients. If the flag is not
	/// set, the function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_FIX_TAUX_TAUY] The coefficients of the tilted sensor model are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// The function estimates the transformation between two cameras making a stereo pair. If one computes
	/// the poses of an object relative to the first camera and to the second camera,
	/// ( ![inline formula](https://latex.codecogs.com/png.latex?R%5F1),![inline formula](https://latex.codecogs.com/png.latex?T%5F1) ) and (![inline formula](https://latex.codecogs.com/png.latex?R%5F2),![inline formula](https://latex.codecogs.com/png.latex?T%5F2)), respectively, for a stereo camera where the
	/// relative position and orientation between the two cameras are fixed, then those poses definitely
	/// relate to each other. This means, if the relative position and orientation (![inline formula](https://latex.codecogs.com/png.latex?R),![inline formula](https://latex.codecogs.com/png.latex?T)) of the
	/// two cameras is known, it is possible to compute (![inline formula](https://latex.codecogs.com/png.latex?R%5F2),![inline formula](https://latex.codecogs.com/png.latex?T%5F2)) when (![inline formula](https://latex.codecogs.com/png.latex?R%5F1),![inline formula](https://latex.codecogs.com/png.latex?T%5F1)) is
	/// given. This is what the described function does. It computes (![inline formula](https://latex.codecogs.com/png.latex?R),![inline formula](https://latex.codecogs.com/png.latex?T)) such that:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?R%5F2%3DR%20R%5F1)
	/// ![block formula](https://latex.codecogs.com/png.latex?T%5F2%3DR%20T%5F1%20%2B%20T%2E)
	///
	/// Therefore, one can compute the coordinate representation of a 3D point for the second camera's
	/// coordinate system when given the point's coordinate representation in the first camera's coordinate
	/// system:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0AX%5F2%20%5C%5C%0AY%5F2%20%5C%5C%0AZ%5F2%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0AR%20%26%20T%20%5C%5C%0A0%20%26%201%0A%5Cend%7Bbmatrix%7D%20%5Cbegin%7Bbmatrix%7D%0AX%5F1%20%5C%5C%0AY%5F1%20%5C%5C%0AZ%5F1%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%2E)
	///
	///
	/// Optionally, it computes the essential matrix E:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?E%3D%20%5Cbegin%7Bbmatrix%7D%200%20%26%20%2DT%5F2%20%26%20T%5F1%5C%5C%20T%5F2%20%26%200%20%26%20%2DT%5F0%5C%5C%20%2DT%5F1%20%26%20T%5F0%20%26%200%20%5Cend%7Bbmatrix%7D%20R)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?T%5Fi) are components of the translation vector ![inline formula](https://latex.codecogs.com/png.latex?T) : ![inline formula](https://latex.codecogs.com/png.latex?T%3D%5BT%5F0%2C%20T%5F1%2C%20T%5F2%5D%5ET) .
	/// And the function can also compute the fundamental matrix F:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?F%20%3D%20cameraMatrix2%5E%7B%2DT%7D%5Ccdot%20E%20%5Ccdot%20cameraMatrix1%5E%7B%2D1%7D)
	///
	/// Besides the stereo-related information, the function can also perform a full calibration of each of
	/// the two cameras. However, due to the high dimensionality of the parameter space and noise in the
	/// input data, the function can diverge from the correct solution. If the intrinsic parameters can be
	/// estimated with high accuracy for each of the cameras individually (for example, using
	/// [calibrate_camera] ), you are recommended to do so and then pass [CALIB_FIX_INTRINSIC] flag to the
	/// function along with the computed intrinsic parameters. Otherwise, if all the parameters are
	/// estimated at once, it makes sense to restrict some parameters, for example, pass
	///  [CALIB_SAME_FOCAL_LENGTH] and [CALIB_ZERO_TANGENT_DIST] flags, which is usually a
	/// reasonable assumption.
	///
	/// Similarly to #calibrateCamera, the function minimizes the total re-projection error for all the
	/// points in all the available views from both cameras. The function returns the final value of the
	/// re-projection error.
	///
	/// ## C++ default parameters
	/// * flags: CALIB_FIX_INTRINSIC
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,30,1e-6)
	#[inline]
	pub fn stereo_calibrate_extended(object_points: &impl ToInputArray, image_points1: &impl ToInputArray, image_points2: &impl ToInputArray, camera_matrix1: &mut impl ToInputOutputArray, dist_coeffs1: &mut impl ToInputOutputArray, camera_matrix2: &mut impl ToInputOutputArray, dist_coeffs2: &mut impl ToInputOutputArray, image_size: core::Size, r: &mut impl ToInputOutputArray, t: &mut impl ToInputOutputArray, e: &mut impl ToOutputArray, f: &mut impl ToOutputArray, rvecs: &mut impl ToOutputArray, tvecs: &mut impl ToOutputArray, per_view_errors: &mut impl ToOutputArray, flags: i32, criteria: core::TermCriteria) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points1);
		input_array_arg!(image_points2);
		input_output_array_arg!(camera_matrix1);
		input_output_array_arg!(dist_coeffs1);
		input_output_array_arg!(camera_matrix2);
		input_output_array_arg!(dist_coeffs2);
		input_output_array_arg!(r);
		input_output_array_arg!(t);
		output_array_arg!(e);
		output_array_arg!(f);
		output_array_arg!(rvecs);
		output_array_arg!(tvecs);
		output_array_arg!(per_view_errors);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_stereoCalibrate_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_Size_const__InputOutputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_TermCriteria(object_points.as_raw__InputArray(), image_points1.as_raw__InputArray(), image_points2.as_raw__InputArray(), camera_matrix1.as_raw__InputOutputArray(), dist_coeffs1.as_raw__InputOutputArray(), camera_matrix2.as_raw__InputOutputArray(), dist_coeffs2.as_raw__InputOutputArray(), &image_size, r.as_raw__InputOutputArray(), t.as_raw__InputOutputArray(), e.as_raw__OutputArray(), f.as_raw__OutputArray(), rvecs.as_raw__OutputArray(), tvecs.as_raw__OutputArray(), per_view_errors.as_raw__OutputArray(), flags, &criteria, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Calibrates a stereo camera set up. This function finds the intrinsic parameters
	/// for each of the two cameras and the extrinsic parameters between the two cameras.
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of the calibration pattern points. The same structure as
	/// in [calibrateCamera]. For each pattern view, both cameras need to see the same object
	/// points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be
	/// equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to
	/// be equal for each i.
	/// * imagePoints1: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the first camera. The same structure as in [calibrateCamera].
	/// * imagePoints2: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the second camera. The same structure as in [calibrateCamera].
	/// * cameraMatrix1: Input/output camera intrinsic matrix for the first camera, the same as in
	/// [calibrateCamera]. Furthermore, for the stereo case, additional flags may be used, see below.
	/// * distCoeffs1: Input/output vector of distortion coefficients, the same as in
	/// [calibrateCamera].
	/// * cameraMatrix2: Input/output second camera intrinsic matrix for the second camera. See description for
	/// cameraMatrix1.
	/// * distCoeffs2: Input/output lens distortion coefficients for the second camera. See
	/// description for distCoeffs1.
	/// * imageSize: Size of the image used only to initialize the camera intrinsic matrices.
	/// * R: Output rotation matrix. Together with the translation vector T, this matrix brings
	/// points given in the first camera's coordinate system to points in the second camera's
	/// coordinate system. In more technical terms, the tuple of R and T performs a change of basis
	/// from the first camera's coordinate system to the second camera's coordinate system. Due to its
	/// duality, this tuple is equivalent to the position of the first camera with respect to the
	/// second camera coordinate system.
	/// * T: Output translation vector, see description above.
	/// * E: Output essential matrix.
	/// * F: Output fundamental matrix.
	/// * rvecs: Output vector of rotation vectors ( [Rodrigues] ) estimated for each pattern view in the
	/// coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
	/// i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
	/// description) brings the calibration pattern from the object coordinate space (in which object points are
	/// specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
	/// the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
	/// to camera coordinate space of the first camera of the stereo pair.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view, see parameter description
	/// of previous output parameter ( rvecs ).
	/// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of the following values:
	/// *   [CALIB_FIX_INTRINSIC] Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F
	/// matrices are estimated.
	/// *   [CALIB_USE_INTRINSIC_GUESS] Optimize some or all of the intrinsic parameters
	/// according to the specified flags. Initial values are provided by the user.
	/// *   [CALIB_USE_EXTRINSIC_GUESS] R and T contain valid initial values that are optimized further.
	/// Otherwise R and T are initialized to the median value of the pattern views (each dimension separately).
	/// *   [CALIB_FIX_PRINCIPAL_POINT] Fix the principal points during the optimization.
	/// *   [CALIB_FIX_FOCAL_LENGTH] Fix ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fy) .
	/// *   [CALIB_FIX_ASPECT_RATIO] Optimize ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fy) . Fix the ratio ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fx%2Ff%5E%7B%28j%29%7D%5Fy)
	/// .
	/// *   [CALIB_SAME_FOCAL_LENGTH] Enforce ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%280%29%7D%5Fx%3Df%5E%7B%281%29%7D%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%280%29%7D%5Fy%3Df%5E%7B%281%29%7D%5Fy) .
	/// *   [CALIB_ZERO_TANGENT_DIST] Set tangential distortion coefficients for each camera to
	/// zeros and fix there.
	/// *   [CALIB_FIX_K1],..., [CALIB_FIX_K6] Do not change the corresponding radial
	/// distortion coefficient during the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set,
	/// the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_RATIONAL_MODEL] Enable coefficients k4, k5, and k6. To provide the backward
	/// compatibility, this extra flag should be explicitly specified to make the calibration
	/// function use the rational model and return 8 coefficients. If the flag is not set, the
	/// function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_THIN_PRISM_MODEL] Coefficients s1, s2, s3 and s4 are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the thin prism model and return 12 coefficients. If the flag is not
	/// set, the function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_FIX_S1_S2_S3_S4] The thin prism distortion coefficients are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_TILTED_MODEL] Coefficients tauX and tauY are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the tilted sensor model and return 14 coefficients. If the flag is not
	/// set, the function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_FIX_TAUX_TAUY] The coefficients of the tilted sensor model are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// The function estimates the transformation between two cameras making a stereo pair. If one computes
	/// the poses of an object relative to the first camera and to the second camera,
	/// ( ![inline formula](https://latex.codecogs.com/png.latex?R%5F1),![inline formula](https://latex.codecogs.com/png.latex?T%5F1) ) and (![inline formula](https://latex.codecogs.com/png.latex?R%5F2),![inline formula](https://latex.codecogs.com/png.latex?T%5F2)), respectively, for a stereo camera where the
	/// relative position and orientation between the two cameras are fixed, then those poses definitely
	/// relate to each other. This means, if the relative position and orientation (![inline formula](https://latex.codecogs.com/png.latex?R),![inline formula](https://latex.codecogs.com/png.latex?T)) of the
	/// two cameras is known, it is possible to compute (![inline formula](https://latex.codecogs.com/png.latex?R%5F2),![inline formula](https://latex.codecogs.com/png.latex?T%5F2)) when (![inline formula](https://latex.codecogs.com/png.latex?R%5F1),![inline formula](https://latex.codecogs.com/png.latex?T%5F1)) is
	/// given. This is what the described function does. It computes (![inline formula](https://latex.codecogs.com/png.latex?R),![inline formula](https://latex.codecogs.com/png.latex?T)) such that:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?R%5F2%3DR%20R%5F1)
	/// ![block formula](https://latex.codecogs.com/png.latex?T%5F2%3DR%20T%5F1%20%2B%20T%2E)
	///
	/// Therefore, one can compute the coordinate representation of a 3D point for the second camera's
	/// coordinate system when given the point's coordinate representation in the first camera's coordinate
	/// system:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0AX%5F2%20%5C%5C%0AY%5F2%20%5C%5C%0AZ%5F2%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0AR%20%26%20T%20%5C%5C%0A0%20%26%201%0A%5Cend%7Bbmatrix%7D%20%5Cbegin%7Bbmatrix%7D%0AX%5F1%20%5C%5C%0AY%5F1%20%5C%5C%0AZ%5F1%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%2E)
	///
	///
	/// Optionally, it computes the essential matrix E:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?E%3D%20%5Cbegin%7Bbmatrix%7D%200%20%26%20%2DT%5F2%20%26%20T%5F1%5C%5C%20T%5F2%20%26%200%20%26%20%2DT%5F0%5C%5C%20%2DT%5F1%20%26%20T%5F0%20%26%200%20%5Cend%7Bbmatrix%7D%20R)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?T%5Fi) are components of the translation vector ![inline formula](https://latex.codecogs.com/png.latex?T) : ![inline formula](https://latex.codecogs.com/png.latex?T%3D%5BT%5F0%2C%20T%5F1%2C%20T%5F2%5D%5ET) .
	/// And the function can also compute the fundamental matrix F:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?F%20%3D%20cameraMatrix2%5E%7B%2DT%7D%5Ccdot%20E%20%5Ccdot%20cameraMatrix1%5E%7B%2D1%7D)
	///
	/// Besides the stereo-related information, the function can also perform a full calibration of each of
	/// the two cameras. However, due to the high dimensionality of the parameter space and noise in the
	/// input data, the function can diverge from the correct solution. If the intrinsic parameters can be
	/// estimated with high accuracy for each of the cameras individually (for example, using
	/// [calibrate_camera] ), you are recommended to do so and then pass [CALIB_FIX_INTRINSIC] flag to the
	/// function along with the computed intrinsic parameters. Otherwise, if all the parameters are
	/// estimated at once, it makes sense to restrict some parameters, for example, pass
	///  [CALIB_SAME_FOCAL_LENGTH] and [CALIB_ZERO_TANGENT_DIST] flags, which is usually a
	/// reasonable assumption.
	///
	/// Similarly to #calibrateCamera, the function minimizes the total re-projection error for all the
	/// points in all the available views from both cameras. The function returns the final value of the
	/// re-projection error.
	///
	/// ## Overloaded parameters
	///
	/// ## C++ default parameters
	/// * flags: CALIB_FIX_INTRINSIC
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,30,1e-6)
	#[inline]
	pub fn stereo_calibrate_1(object_points: &impl ToInputArray, image_points1: &impl ToInputArray, image_points2: &impl ToInputArray, camera_matrix1: &mut impl ToInputOutputArray, dist_coeffs1: &mut impl ToInputOutputArray, camera_matrix2: &mut impl ToInputOutputArray, dist_coeffs2: &mut impl ToInputOutputArray, image_size: core::Size, r: &mut impl ToInputOutputArray, t: &mut impl ToInputOutputArray, e: &mut impl ToOutputArray, f: &mut impl ToOutputArray, per_view_errors: &mut impl ToOutputArray, flags: i32, criteria: core::TermCriteria) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points1);
		input_array_arg!(image_points2);
		input_output_array_arg!(camera_matrix1);
		input_output_array_arg!(dist_coeffs1);
		input_output_array_arg!(camera_matrix2);
		input_output_array_arg!(dist_coeffs2);
		input_output_array_arg!(r);
		input_output_array_arg!(t);
		output_array_arg!(e);
		output_array_arg!(f);
		output_array_arg!(per_view_errors);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_stereoCalibrate_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_Size_const__InputOutputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_TermCriteria(object_points.as_raw__InputArray(), image_points1.as_raw__InputArray(), image_points2.as_raw__InputArray(), camera_matrix1.as_raw__InputOutputArray(), dist_coeffs1.as_raw__InputOutputArray(), camera_matrix2.as_raw__InputOutputArray(), dist_coeffs2.as_raw__InputOutputArray(), &image_size, r.as_raw__InputOutputArray(), t.as_raw__InputOutputArray(), e.as_raw__OutputArray(), f.as_raw__OutputArray(), per_view_errors.as_raw__OutputArray(), flags, &criteria, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Calibrates a stereo camera set up. This function finds the intrinsic parameters
	/// for each of the two cameras and the extrinsic parameters between the two cameras.
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of the calibration pattern points. The same structure as
	/// in [calibrateCamera]. For each pattern view, both cameras need to see the same object
	/// points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be
	/// equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to
	/// be equal for each i.
	/// * imagePoints1: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the first camera. The same structure as in [calibrateCamera].
	/// * imagePoints2: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the second camera. The same structure as in [calibrateCamera].
	/// * cameraMatrix1: Input/output camera intrinsic matrix for the first camera, the same as in
	/// [calibrateCamera]. Furthermore, for the stereo case, additional flags may be used, see below.
	/// * distCoeffs1: Input/output vector of distortion coefficients, the same as in
	/// [calibrateCamera].
	/// * cameraMatrix2: Input/output second camera intrinsic matrix for the second camera. See description for
	/// cameraMatrix1.
	/// * distCoeffs2: Input/output lens distortion coefficients for the second camera. See
	/// description for distCoeffs1.
	/// * imageSize: Size of the image used only to initialize the camera intrinsic matrices.
	/// * R: Output rotation matrix. Together with the translation vector T, this matrix brings
	/// points given in the first camera's coordinate system to points in the second camera's
	/// coordinate system. In more technical terms, the tuple of R and T performs a change of basis
	/// from the first camera's coordinate system to the second camera's coordinate system. Due to its
	/// duality, this tuple is equivalent to the position of the first camera with respect to the
	/// second camera coordinate system.
	/// * T: Output translation vector, see description above.
	/// * E: Output essential matrix.
	/// * F: Output fundamental matrix.
	/// * rvecs: Output vector of rotation vectors ( [Rodrigues] ) estimated for each pattern view in the
	/// coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
	/// i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
	/// description) brings the calibration pattern from the object coordinate space (in which object points are
	/// specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
	/// the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
	/// to camera coordinate space of the first camera of the stereo pair.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view, see parameter description
	/// of previous output parameter ( rvecs ).
	/// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of the following values:
	/// *   [CALIB_FIX_INTRINSIC] Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F
	/// matrices are estimated.
	/// *   [CALIB_USE_INTRINSIC_GUESS] Optimize some or all of the intrinsic parameters
	/// according to the specified flags. Initial values are provided by the user.
	/// *   [CALIB_USE_EXTRINSIC_GUESS] R and T contain valid initial values that are optimized further.
	/// Otherwise R and T are initialized to the median value of the pattern views (each dimension separately).
	/// *   [CALIB_FIX_PRINCIPAL_POINT] Fix the principal points during the optimization.
	/// *   [CALIB_FIX_FOCAL_LENGTH] Fix ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fy) .
	/// *   [CALIB_FIX_ASPECT_RATIO] Optimize ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fy) . Fix the ratio ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fx%2Ff%5E%7B%28j%29%7D%5Fy)
	/// .
	/// *   [CALIB_SAME_FOCAL_LENGTH] Enforce ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%280%29%7D%5Fx%3Df%5E%7B%281%29%7D%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%280%29%7D%5Fy%3Df%5E%7B%281%29%7D%5Fy) .
	/// *   [CALIB_ZERO_TANGENT_DIST] Set tangential distortion coefficients for each camera to
	/// zeros and fix there.
	/// *   [CALIB_FIX_K1],..., [CALIB_FIX_K6] Do not change the corresponding radial
	/// distortion coefficient during the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set,
	/// the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_RATIONAL_MODEL] Enable coefficients k4, k5, and k6. To provide the backward
	/// compatibility, this extra flag should be explicitly specified to make the calibration
	/// function use the rational model and return 8 coefficients. If the flag is not set, the
	/// function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_THIN_PRISM_MODEL] Coefficients s1, s2, s3 and s4 are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the thin prism model and return 12 coefficients. If the flag is not
	/// set, the function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_FIX_S1_S2_S3_S4] The thin prism distortion coefficients are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_TILTED_MODEL] Coefficients tauX and tauY are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the tilted sensor model and return 14 coefficients. If the flag is not
	/// set, the function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_FIX_TAUX_TAUY] The coefficients of the tilted sensor model are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// The function estimates the transformation between two cameras making a stereo pair. If one computes
	/// the poses of an object relative to the first camera and to the second camera,
	/// ( ![inline formula](https://latex.codecogs.com/png.latex?R%5F1),![inline formula](https://latex.codecogs.com/png.latex?T%5F1) ) and (![inline formula](https://latex.codecogs.com/png.latex?R%5F2),![inline formula](https://latex.codecogs.com/png.latex?T%5F2)), respectively, for a stereo camera where the
	/// relative position and orientation between the two cameras are fixed, then those poses definitely
	/// relate to each other. This means, if the relative position and orientation (![inline formula](https://latex.codecogs.com/png.latex?R),![inline formula](https://latex.codecogs.com/png.latex?T)) of the
	/// two cameras is known, it is possible to compute (![inline formula](https://latex.codecogs.com/png.latex?R%5F2),![inline formula](https://latex.codecogs.com/png.latex?T%5F2)) when (![inline formula](https://latex.codecogs.com/png.latex?R%5F1),![inline formula](https://latex.codecogs.com/png.latex?T%5F1)) is
	/// given. This is what the described function does. It computes (![inline formula](https://latex.codecogs.com/png.latex?R),![inline formula](https://latex.codecogs.com/png.latex?T)) such that:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?R%5F2%3DR%20R%5F1)
	/// ![block formula](https://latex.codecogs.com/png.latex?T%5F2%3DR%20T%5F1%20%2B%20T%2E)
	///
	/// Therefore, one can compute the coordinate representation of a 3D point for the second camera's
	/// coordinate system when given the point's coordinate representation in the first camera's coordinate
	/// system:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0AX%5F2%20%5C%5C%0AY%5F2%20%5C%5C%0AZ%5F2%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0AR%20%26%20T%20%5C%5C%0A0%20%26%201%0A%5Cend%7Bbmatrix%7D%20%5Cbegin%7Bbmatrix%7D%0AX%5F1%20%5C%5C%0AY%5F1%20%5C%5C%0AZ%5F1%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%2E)
	///
	///
	/// Optionally, it computes the essential matrix E:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?E%3D%20%5Cbegin%7Bbmatrix%7D%200%20%26%20%2DT%5F2%20%26%20T%5F1%5C%5C%20T%5F2%20%26%200%20%26%20%2DT%5F0%5C%5C%20%2DT%5F1%20%26%20T%5F0%20%26%200%20%5Cend%7Bbmatrix%7D%20R)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?T%5Fi) are components of the translation vector ![inline formula](https://latex.codecogs.com/png.latex?T) : ![inline formula](https://latex.codecogs.com/png.latex?T%3D%5BT%5F0%2C%20T%5F1%2C%20T%5F2%5D%5ET) .
	/// And the function can also compute the fundamental matrix F:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?F%20%3D%20cameraMatrix2%5E%7B%2DT%7D%5Ccdot%20E%20%5Ccdot%20cameraMatrix1%5E%7B%2D1%7D)
	///
	/// Besides the stereo-related information, the function can also perform a full calibration of each of
	/// the two cameras. However, due to the high dimensionality of the parameter space and noise in the
	/// input data, the function can diverge from the correct solution. If the intrinsic parameters can be
	/// estimated with high accuracy for each of the cameras individually (for example, using
	/// [calibrate_camera] ), you are recommended to do so and then pass [CALIB_FIX_INTRINSIC] flag to the
	/// function along with the computed intrinsic parameters. Otherwise, if all the parameters are
	/// estimated at once, it makes sense to restrict some parameters, for example, pass
	///  [CALIB_SAME_FOCAL_LENGTH] and [CALIB_ZERO_TANGENT_DIST] flags, which is usually a
	/// reasonable assumption.
	///
	/// Similarly to #calibrateCamera, the function minimizes the total re-projection error for all the
	/// points in all the available views from both cameras. The function returns the final value of the
	/// re-projection error.
	///
	/// ## Overloaded parameters
	///
	///
	/// ## Note
	/// This alternative version of [stereo_calibrate] function uses the following default values for its arguments:
	/// * flags: CALIB_FIX_INTRINSIC
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,30,1e-6)
	#[inline]
	pub fn stereo_calibrate_def(object_points: &impl ToInputArray, image_points1: &impl ToInputArray, image_points2: &impl ToInputArray, camera_matrix1: &mut impl ToInputOutputArray, dist_coeffs1: &mut impl ToInputOutputArray, camera_matrix2: &mut impl ToInputOutputArray, dist_coeffs2: &mut impl ToInputOutputArray, image_size: core::Size, r: &mut impl ToOutputArray, t: &mut impl ToOutputArray, e: &mut impl ToOutputArray, f: &mut impl ToOutputArray) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points1);
		input_array_arg!(image_points2);
		input_output_array_arg!(camera_matrix1);
		input_output_array_arg!(dist_coeffs1);
		input_output_array_arg!(camera_matrix2);
		input_output_array_arg!(dist_coeffs2);
		output_array_arg!(r);
		output_array_arg!(t);
		output_array_arg!(e);
		output_array_arg!(f);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_stereoCalibrate_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_Size_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(object_points.as_raw__InputArray(), image_points1.as_raw__InputArray(), image_points2.as_raw__InputArray(), camera_matrix1.as_raw__InputOutputArray(), dist_coeffs1.as_raw__InputOutputArray(), camera_matrix2.as_raw__InputOutputArray(), dist_coeffs2.as_raw__InputOutputArray(), &image_size, r.as_raw__OutputArray(), t.as_raw__OutputArray(), e.as_raw__OutputArray(), f.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Calibrates a stereo camera set up. This function finds the intrinsic parameters
	/// for each of the two cameras and the extrinsic parameters between the two cameras.
	///
	/// ## Parameters
	/// * objectPoints: Vector of vectors of the calibration pattern points. The same structure as
	/// in [calibrateCamera]. For each pattern view, both cameras need to see the same object
	/// points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be
	/// equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to
	/// be equal for each i.
	/// * imagePoints1: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the first camera. The same structure as in [calibrateCamera].
	/// * imagePoints2: Vector of vectors of the projections of the calibration pattern points,
	/// observed by the second camera. The same structure as in [calibrateCamera].
	/// * cameraMatrix1: Input/output camera intrinsic matrix for the first camera, the same as in
	/// [calibrateCamera]. Furthermore, for the stereo case, additional flags may be used, see below.
	/// * distCoeffs1: Input/output vector of distortion coefficients, the same as in
	/// [calibrateCamera].
	/// * cameraMatrix2: Input/output second camera intrinsic matrix for the second camera. See description for
	/// cameraMatrix1.
	/// * distCoeffs2: Input/output lens distortion coefficients for the second camera. See
	/// description for distCoeffs1.
	/// * imageSize: Size of the image used only to initialize the camera intrinsic matrices.
	/// * R: Output rotation matrix. Together with the translation vector T, this matrix brings
	/// points given in the first camera's coordinate system to points in the second camera's
	/// coordinate system. In more technical terms, the tuple of R and T performs a change of basis
	/// from the first camera's coordinate system to the second camera's coordinate system. Due to its
	/// duality, this tuple is equivalent to the position of the first camera with respect to the
	/// second camera coordinate system.
	/// * T: Output translation vector, see description above.
	/// * E: Output essential matrix.
	/// * F: Output fundamental matrix.
	/// * rvecs: Output vector of rotation vectors ( [Rodrigues] ) estimated for each pattern view in the
	/// coordinate system of the first camera of the stereo pair (e.g. std::vector<cv::Mat>). More in detail, each
	/// i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter
	/// description) brings the calibration pattern from the object coordinate space (in which object points are
	/// specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms,
	/// the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space
	/// to camera coordinate space of the first camera of the stereo pair.
	/// * tvecs: Output vector of translation vectors estimated for each pattern view, see parameter description
	/// of previous output parameter ( rvecs ).
	/// * perViewErrors: Output vector of the RMS re-projection error estimated for each pattern view.
	/// * flags: Different flags that may be zero or a combination of the following values:
	/// *   [CALIB_FIX_INTRINSIC] Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F
	/// matrices are estimated.
	/// *   [CALIB_USE_INTRINSIC_GUESS] Optimize some or all of the intrinsic parameters
	/// according to the specified flags. Initial values are provided by the user.
	/// *   [CALIB_USE_EXTRINSIC_GUESS] R and T contain valid initial values that are optimized further.
	/// Otherwise R and T are initialized to the median value of the pattern views (each dimension separately).
	/// *   [CALIB_FIX_PRINCIPAL_POINT] Fix the principal points during the optimization.
	/// *   [CALIB_FIX_FOCAL_LENGTH] Fix ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fy) .
	/// *   [CALIB_FIX_ASPECT_RATIO] Optimize ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fy) . Fix the ratio ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%28j%29%7D%5Fx%2Ff%5E%7B%28j%29%7D%5Fy)
	/// .
	/// *   [CALIB_SAME_FOCAL_LENGTH] Enforce ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%280%29%7D%5Fx%3Df%5E%7B%281%29%7D%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?f%5E%7B%280%29%7D%5Fy%3Df%5E%7B%281%29%7D%5Fy) .
	/// *   [CALIB_ZERO_TANGENT_DIST] Set tangential distortion coefficients for each camera to
	/// zeros and fix there.
	/// *   [CALIB_FIX_K1],..., [CALIB_FIX_K6] Do not change the corresponding radial
	/// distortion coefficient during the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set,
	/// the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_RATIONAL_MODEL] Enable coefficients k4, k5, and k6. To provide the backward
	/// compatibility, this extra flag should be explicitly specified to make the calibration
	/// function use the rational model and return 8 coefficients. If the flag is not set, the
	/// function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_THIN_PRISM_MODEL] Coefficients s1, s2, s3 and s4 are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the thin prism model and return 12 coefficients. If the flag is not
	/// set, the function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_FIX_S1_S2_S3_S4] The thin prism distortion coefficients are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// *   [CALIB_TILTED_MODEL] Coefficients tauX and tauY are enabled. To provide the
	/// backward compatibility, this extra flag should be explicitly specified to make the
	/// calibration function use the tilted sensor model and return 14 coefficients. If the flag is not
	/// set, the function computes and returns only 5 distortion coefficients.
	/// *   [CALIB_FIX_TAUX_TAUY] The coefficients of the tilted sensor model are not changed during
	/// the optimization. If [CALIB_USE_INTRINSIC_GUESS] is set, the coefficient from the
	/// supplied distCoeffs matrix is used. Otherwise, it is set to 0.
	/// * criteria: Termination criteria for the iterative optimization algorithm.
	///
	/// The function estimates the transformation between two cameras making a stereo pair. If one computes
	/// the poses of an object relative to the first camera and to the second camera,
	/// ( ![inline formula](https://latex.codecogs.com/png.latex?R%5F1),![inline formula](https://latex.codecogs.com/png.latex?T%5F1) ) and (![inline formula](https://latex.codecogs.com/png.latex?R%5F2),![inline formula](https://latex.codecogs.com/png.latex?T%5F2)), respectively, for a stereo camera where the
	/// relative position and orientation between the two cameras are fixed, then those poses definitely
	/// relate to each other. This means, if the relative position and orientation (![inline formula](https://latex.codecogs.com/png.latex?R),![inline formula](https://latex.codecogs.com/png.latex?T)) of the
	/// two cameras is known, it is possible to compute (![inline formula](https://latex.codecogs.com/png.latex?R%5F2),![inline formula](https://latex.codecogs.com/png.latex?T%5F2)) when (![inline formula](https://latex.codecogs.com/png.latex?R%5F1),![inline formula](https://latex.codecogs.com/png.latex?T%5F1)) is
	/// given. This is what the described function does. It computes (![inline formula](https://latex.codecogs.com/png.latex?R),![inline formula](https://latex.codecogs.com/png.latex?T)) such that:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?R%5F2%3DR%20R%5F1)
	/// ![block formula](https://latex.codecogs.com/png.latex?T%5F2%3DR%20T%5F1%20%2B%20T%2E)
	///
	/// Therefore, one can compute the coordinate representation of a 3D point for the second camera's
	/// coordinate system when given the point's coordinate representation in the first camera's coordinate
	/// system:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%0AX%5F2%20%5C%5C%0AY%5F2%20%5C%5C%0AZ%5F2%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0AR%20%26%20T%20%5C%5C%0A0%20%26%201%0A%5Cend%7Bbmatrix%7D%20%5Cbegin%7Bbmatrix%7D%0AX%5F1%20%5C%5C%0AY%5F1%20%5C%5C%0AZ%5F1%20%5C%5C%0A1%0A%5Cend%7Bbmatrix%7D%2E)
	///
	///
	/// Optionally, it computes the essential matrix E:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?E%3D%20%5Cbegin%7Bbmatrix%7D%200%20%26%20%2DT%5F2%20%26%20T%5F1%5C%5C%20T%5F2%20%26%200%20%26%20%2DT%5F0%5C%5C%20%2DT%5F1%20%26%20T%5F0%20%26%200%20%5Cend%7Bbmatrix%7D%20R)
	///
	/// where ![inline formula](https://latex.codecogs.com/png.latex?T%5Fi) are components of the translation vector ![inline formula](https://latex.codecogs.com/png.latex?T) : ![inline formula](https://latex.codecogs.com/png.latex?T%3D%5BT%5F0%2C%20T%5F1%2C%20T%5F2%5D%5ET) .
	/// And the function can also compute the fundamental matrix F:
	///
	/// ![block formula](https://latex.codecogs.com/png.latex?F%20%3D%20cameraMatrix2%5E%7B%2DT%7D%5Ccdot%20E%20%5Ccdot%20cameraMatrix1%5E%7B%2D1%7D)
	///
	/// Besides the stereo-related information, the function can also perform a full calibration of each of
	/// the two cameras. However, due to the high dimensionality of the parameter space and noise in the
	/// input data, the function can diverge from the correct solution. If the intrinsic parameters can be
	/// estimated with high accuracy for each of the cameras individually (for example, using
	/// [calibrate_camera] ), you are recommended to do so and then pass [CALIB_FIX_INTRINSIC] flag to the
	/// function along with the computed intrinsic parameters. Otherwise, if all the parameters are
	/// estimated at once, it makes sense to restrict some parameters, for example, pass
	///  [CALIB_SAME_FOCAL_LENGTH] and [CALIB_ZERO_TANGENT_DIST] flags, which is usually a
	/// reasonable assumption.
	///
	/// Similarly to #calibrateCamera, the function minimizes the total re-projection error for all the
	/// points in all the available views from both cameras. The function returns the final value of the
	/// re-projection error.
	///
	/// ## Overloaded parameters
	///
	/// ## C++ default parameters
	/// * flags: CALIB_FIX_INTRINSIC
	/// * criteria: TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,30,1e-6)
	#[inline]
	pub fn stereo_calibrate(object_points: &impl ToInputArray, image_points1: &impl ToInputArray, image_points2: &impl ToInputArray, camera_matrix1: &mut impl ToInputOutputArray, dist_coeffs1: &mut impl ToInputOutputArray, camera_matrix2: &mut impl ToInputOutputArray, dist_coeffs2: &mut impl ToInputOutputArray, image_size: core::Size, r: &mut impl ToOutputArray, t: &mut impl ToOutputArray, e: &mut impl ToOutputArray, f: &mut impl ToOutputArray, flags: i32, criteria: core::TermCriteria) -> Result<f64> {
		input_array_arg!(object_points);
		input_array_arg!(image_points1);
		input_array_arg!(image_points2);
		input_output_array_arg!(camera_matrix1);
		input_output_array_arg!(dist_coeffs1);
		input_output_array_arg!(camera_matrix2);
		input_output_array_arg!(dist_coeffs2);
		output_array_arg!(r);
		output_array_arg!(t);
		output_array_arg!(e);
		output_array_arg!(f);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_stereoCalibrate_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_const__InputOutputArrayR_Size_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_TermCriteria(object_points.as_raw__InputArray(), image_points1.as_raw__InputArray(), image_points2.as_raw__InputArray(), camera_matrix1.as_raw__InputOutputArray(), dist_coeffs1.as_raw__InputOutputArray(), camera_matrix2.as_raw__InputOutputArray(), dist_coeffs2.as_raw__InputOutputArray(), &image_size, r.as_raw__OutputArray(), t.as_raw__OutputArray(), e.as_raw__OutputArray(), f.as_raw__OutputArray(), flags, &criteria, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes a rectification transform for an uncalibrated stereo camera.
	///
	/// ## Parameters
	/// * points1: Array of feature points in the first image.
	/// * points2: The corresponding points in the second image. The same formats as in
	/// [find_fundamental_mat] are supported.
	/// * F: Input fundamental matrix. It can be computed from the same set of point pairs using
	/// [find_fundamental_mat] .
	/// * imgSize: Size of the image.
	/// * H1: Output rectification homography matrix for the first image.
	/// * H2: Output rectification homography matrix for the second image.
	/// * threshold: Optional threshold used to filter out the outliers. If the parameter is greater
	/// than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points
	/// for which ![inline formula](https://latex.codecogs.com/png.latex?%7C%5Ctexttt%7Bpoints2%5Bi%5D%7D%5ET%20%5Ccdot%20%5Ctexttt%7BF%7D%20%5Ccdot%20%5Ctexttt%7Bpoints1%5Bi%5D%7D%7C%3E%5Ctexttt%7Bthreshold%7D) )
	/// are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.
	///
	/// The function computes the rectification transformations without knowing intrinsic parameters of the
	/// cameras and their relative position in the space, which explains the suffix "uncalibrated". Another
	/// related difference from [stereo_rectify] is that the function outputs not the rectification
	/// transformations in the object (3D) space, but the planar perspective transformations encoded by the
	/// homography matrices H1 and H2 . The function implements the algorithm [Hartley99](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Hartley99) .
	///
	///
	/// Note:
	///    While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily
	///    depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion,
	///    it would be better to correct it before computing the fundamental matrix and calling this
	///    function. For example, distortion coefficients can be estimated for each head of stereo camera
	///    separately by using [calibrate_camera] . Then, the images can be corrected using [undistort] , or
	///    just the point coordinates can be corrected with [undistort_points] .
	///
	/// ## Note
	/// This alternative version of [stereo_rectify_uncalibrated] function uses the following default values for its arguments:
	/// * threshold: 5
	#[inline]
	pub fn stereo_rectify_uncalibrated_def(points1: &impl ToInputArray, points2: &impl ToInputArray, f: &impl ToInputArray, img_size: core::Size, h1: &mut impl ToOutputArray, h2: &mut impl ToOutputArray) -> Result<bool> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		input_array_arg!(f);
		output_array_arg!(h1);
		output_array_arg!(h2);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_stereoRectifyUncalibrated_const__InputArrayR_const__InputArrayR_const__InputArrayR_Size_const__OutputArrayR_const__OutputArrayR(points1.as_raw__InputArray(), points2.as_raw__InputArray(), f.as_raw__InputArray(), &img_size, h1.as_raw__OutputArray(), h2.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes a rectification transform for an uncalibrated stereo camera.
	///
	/// ## Parameters
	/// * points1: Array of feature points in the first image.
	/// * points2: The corresponding points in the second image. The same formats as in
	/// [find_fundamental_mat] are supported.
	/// * F: Input fundamental matrix. It can be computed from the same set of point pairs using
	/// [find_fundamental_mat] .
	/// * imgSize: Size of the image.
	/// * H1: Output rectification homography matrix for the first image.
	/// * H2: Output rectification homography matrix for the second image.
	/// * threshold: Optional threshold used to filter out the outliers. If the parameter is greater
	/// than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points
	/// for which ![inline formula](https://latex.codecogs.com/png.latex?%7C%5Ctexttt%7Bpoints2%5Bi%5D%7D%5ET%20%5Ccdot%20%5Ctexttt%7BF%7D%20%5Ccdot%20%5Ctexttt%7Bpoints1%5Bi%5D%7D%7C%3E%5Ctexttt%7Bthreshold%7D) )
	/// are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.
	///
	/// The function computes the rectification transformations without knowing intrinsic parameters of the
	/// cameras and their relative position in the space, which explains the suffix "uncalibrated". Another
	/// related difference from [stereo_rectify] is that the function outputs not the rectification
	/// transformations in the object (3D) space, but the planar perspective transformations encoded by the
	/// homography matrices H1 and H2 . The function implements the algorithm [Hartley99](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_Hartley99) .
	///
	///
	/// Note:
	///    While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily
	///    depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion,
	///    it would be better to correct it before computing the fundamental matrix and calling this
	///    function. For example, distortion coefficients can be estimated for each head of stereo camera
	///    separately by using [calibrate_camera] . Then, the images can be corrected using [undistort] , or
	///    just the point coordinates can be corrected with [undistort_points] .
	///
	/// ## C++ default parameters
	/// * threshold: 5
	#[inline]
	pub fn stereo_rectify_uncalibrated(points1: &impl ToInputArray, points2: &impl ToInputArray, f: &impl ToInputArray, img_size: core::Size, h1: &mut impl ToOutputArray, h2: &mut impl ToOutputArray, threshold: f64) -> Result<bool> {
		input_array_arg!(points1);
		input_array_arg!(points2);
		input_array_arg!(f);
		output_array_arg!(h1);
		output_array_arg!(h2);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_stereoRectifyUncalibrated_const__InputArrayR_const__InputArrayR_const__InputArrayR_Size_const__OutputArrayR_const__OutputArrayR_double(points1.as_raw__InputArray(), points2.as_raw__InputArray(), f.as_raw__InputArray(), &img_size, h1.as_raw__OutputArray(), h2.as_raw__OutputArray(), threshold, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes rectification transforms for each head of a calibrated stereo camera.
	///
	/// ## Parameters
	/// * cameraMatrix1: First camera intrinsic matrix.
	/// * distCoeffs1: First camera distortion parameters.
	/// * cameraMatrix2: Second camera intrinsic matrix.
	/// * distCoeffs2: Second camera distortion parameters.
	/// * imageSize: Size of the image used for stereo calibration.
	/// * R: Rotation matrix from the coordinate system of the first camera to the second camera,
	/// see [stereoCalibrate].
	/// * T: Translation vector from the coordinate system of the first camera to the second camera,
	/// see [stereoCalibrate].
	/// * R1: Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix
	/// brings points given in the unrectified first camera's coordinate system to points in the rectified
	/// first camera's coordinate system. In more technical terms, it performs a change of basis from the
	/// unrectified first camera's coordinate system to the rectified first camera's coordinate system.
	/// * R2: Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix
	/// brings points given in the unrectified second camera's coordinate system to points in the rectified
	/// second camera's coordinate system. In more technical terms, it performs a change of basis from the
	/// unrectified second camera's coordinate system to the rectified second camera's coordinate system.
	/// * P1: Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
	/// camera, i.e. it projects points given in the rectified first camera coordinate system into the
	/// rectified first camera's image.
	/// * P2: Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
	/// camera, i.e. it projects points given in the rectified first camera coordinate system into the
	/// rectified second camera's image.
	/// * Q: Output ![inline formula](https://latex.codecogs.com/png.latex?4%20%5Ctimes%204) disparity-to-depth mapping matrix (see [reprojectImageTo3D]).
	/// * flags: Operation flags that may be zero or [CALIB_ZERO_DISPARITY] . If the flag is set,
	/// the function makes the principal points of each camera have the same pixel coordinates in the
	/// rectified views. And if the flag is not set, the function may still shift the images in the
	/// horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
	/// useful image area.
	/// * alpha: Free scaling parameter. If it is -1 or absent, the function performs the default
	/// scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified
	/// images are zoomed and shifted so that only valid pixels are visible (no black areas after
	/// rectification). alpha=1 means that the rectified image is decimated and shifted so that all the
	/// pixels from the original images from the cameras are retained in the rectified images (no source
	/// image pixels are lost). Any intermediate value yields an intermediate result between
	/// those two extreme cases.
	/// * newImageSize: New image resolution after rectification. The same size should be passed to
	/// [init_undistort_rectify_map] (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
	/// is passed (default), it is set to the original imageSize . Setting it to a larger value can help you
	/// preserve details in the original image, especially when there is a big radial distortion.
	/// * validPixROI1: Optional output rectangles inside the rectified images where all the pixels
	/// are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
	/// (see the picture below).
	/// * validPixROI2: Optional output rectangles inside the rectified images where all the pixels
	/// are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
	/// (see the picture below).
	///
	/// The function computes the rotation matrices for each camera that (virtually) make both camera image
	/// planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies
	/// the dense stereo correspondence problem. The function takes the matrices computed by [stereo_calibrate]
	/// as input. As output, it provides two rotation matrices and also two projection matrices in the new
	/// coordinates. The function distinguishes the following two cases:
	///
	/// *   **Horizontal stereo**: the first and the second camera views are shifted relative to each other
	///    mainly along the x-axis (with possible small vertical shift). In the rectified images, the
	///    corresponding epipolar lines in the left and right cameras are horizontal and have the same
	///    y-coordinate. P1 and P2 look like:
	///
	///    ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BP1%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20f%20%26%200%20%26%20cx%5F1%20%26%200%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%20f%20%26%20cy%20%26%200%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%201%20%26%200%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5Cend%7Bbmatrix%7D)
	///
	///    ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BP2%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20f%20%26%200%20%26%20cx%5F2%20%26%20T%5Fx%20%5Ccdot%20f%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%20f%20%26%20cy%20%26%200%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%201%20%26%200%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5Cend%7Bbmatrix%7D%20%2C)
	///
	///    ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BQ%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%201%20%26%200%20%26%200%20%26%20%2Dcx%5F1%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%201%20%26%200%20%26%20%2Dcy%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%200%20%26%20f%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%20%2D%5Cfrac%7B1%7D%7BT%5Fx%7D%20%26%20%5Cfrac%7Bcx%5F1%20%2D%20cx%5F2%7D%7BT%5Fx%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5Cend%7Bbmatrix%7D%20)
	///
	///    where ![inline formula](https://latex.codecogs.com/png.latex?T%5Fx) is a horizontal shift between the cameras and ![inline formula](https://latex.codecogs.com/png.latex?cx%5F1%3Dcx%5F2) if
	///    [CALIB_ZERO_DISPARITY] is set.
	///
	/// *   **Vertical stereo**: the first and the second camera views are shifted relative to each other
	///    mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar
	///    lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:
	///
	///    ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BP1%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20f%20%26%200%20%26%20cx%20%26%200%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%20f%20%26%20cy%5F1%20%26%200%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%201%20%26%200%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5Cend%7Bbmatrix%7D)
	///
	///    ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BP2%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20f%20%26%200%20%26%20cx%20%26%200%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%20f%20%26%20cy%5F2%20%26%20T%5Fy%20%5Ccdot%20f%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%201%20%26%200%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5Cend%7Bbmatrix%7D%2C)
	///
	///    ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BQ%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%201%20%26%200%20%26%200%20%26%20%2Dcx%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%201%20%26%200%20%26%20%2Dcy%5F1%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%200%20%26%20f%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%20%2D%5Cfrac%7B1%7D%7BT%5Fy%7D%20%26%20%5Cfrac%7Bcy%5F1%20%2D%20cy%5F2%7D%7BT%5Fy%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5Cend%7Bbmatrix%7D%20)
	///
	///    where ![inline formula](https://latex.codecogs.com/png.latex?T%5Fy) is a vertical shift between the cameras and ![inline formula](https://latex.codecogs.com/png.latex?cy%5F1%3Dcy%5F2) if
	///    [CALIB_ZERO_DISPARITY] is set.
	///
	/// As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera
	/// matrices. The matrices, together with R1 and R2 , can then be passed to [init_undistort_rectify_map] to
	/// initialize the rectification map for each camera.
	///
	/// See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through
	/// the corresponding image regions. This means that the images are well rectified, which is what most
	/// stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that
	/// their interiors are all valid pixels.
	///
	/// ![image](https://docs.opencv.org/4.12.0/stereo_undistort.jpg)
	///
	/// ## Note
	/// This alternative version of [stereo_rectify] function uses the following default values for its arguments:
	/// * flags: CALIB_ZERO_DISPARITY
	/// * alpha: -1
	/// * new_image_size: Size()
	/// * valid_pix_roi1: 0
	/// * valid_pix_roi2: 0
	#[inline]
	pub fn stereo_rectify_def(camera_matrix1: &impl ToInputArray, dist_coeffs1: &impl ToInputArray, camera_matrix2: &impl ToInputArray, dist_coeffs2: &impl ToInputArray, image_size: core::Size, r: &impl ToInputArray, t: &impl ToInputArray, r1: &mut impl ToOutputArray, r2: &mut impl ToOutputArray, p1: &mut impl ToOutputArray, p2: &mut impl ToOutputArray, q: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(camera_matrix1);
		input_array_arg!(dist_coeffs1);
		input_array_arg!(camera_matrix2);
		input_array_arg!(dist_coeffs2);
		input_array_arg!(r);
		input_array_arg!(t);
		output_array_arg!(r1);
		output_array_arg!(r2);
		output_array_arg!(p1);
		output_array_arg!(p2);
		output_array_arg!(q);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_stereoRectify_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_Size_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(camera_matrix1.as_raw__InputArray(), dist_coeffs1.as_raw__InputArray(), camera_matrix2.as_raw__InputArray(), dist_coeffs2.as_raw__InputArray(), &image_size, r.as_raw__InputArray(), t.as_raw__InputArray(), r1.as_raw__OutputArray(), r2.as_raw__OutputArray(), p1.as_raw__OutputArray(), p2.as_raw__OutputArray(), q.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes rectification transforms for each head of a calibrated stereo camera.
	///
	/// ## Parameters
	/// * cameraMatrix1: First camera intrinsic matrix.
	/// * distCoeffs1: First camera distortion parameters.
	/// * cameraMatrix2: Second camera intrinsic matrix.
	/// * distCoeffs2: Second camera distortion parameters.
	/// * imageSize: Size of the image used for stereo calibration.
	/// * R: Rotation matrix from the coordinate system of the first camera to the second camera,
	/// see [stereoCalibrate].
	/// * T: Translation vector from the coordinate system of the first camera to the second camera,
	/// see [stereoCalibrate].
	/// * R1: Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix
	/// brings points given in the unrectified first camera's coordinate system to points in the rectified
	/// first camera's coordinate system. In more technical terms, it performs a change of basis from the
	/// unrectified first camera's coordinate system to the rectified first camera's coordinate system.
	/// * R2: Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix
	/// brings points given in the unrectified second camera's coordinate system to points in the rectified
	/// second camera's coordinate system. In more technical terms, it performs a change of basis from the
	/// unrectified second camera's coordinate system to the rectified second camera's coordinate system.
	/// * P1: Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
	/// camera, i.e. it projects points given in the rectified first camera coordinate system into the
	/// rectified first camera's image.
	/// * P2: Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
	/// camera, i.e. it projects points given in the rectified first camera coordinate system into the
	/// rectified second camera's image.
	/// * Q: Output ![inline formula](https://latex.codecogs.com/png.latex?4%20%5Ctimes%204) disparity-to-depth mapping matrix (see [reprojectImageTo3D]).
	/// * flags: Operation flags that may be zero or [CALIB_ZERO_DISPARITY] . If the flag is set,
	/// the function makes the principal points of each camera have the same pixel coordinates in the
	/// rectified views. And if the flag is not set, the function may still shift the images in the
	/// horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
	/// useful image area.
	/// * alpha: Free scaling parameter. If it is -1 or absent, the function performs the default
	/// scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified
	/// images are zoomed and shifted so that only valid pixels are visible (no black areas after
	/// rectification). alpha=1 means that the rectified image is decimated and shifted so that all the
	/// pixels from the original images from the cameras are retained in the rectified images (no source
	/// image pixels are lost). Any intermediate value yields an intermediate result between
	/// those two extreme cases.
	/// * newImageSize: New image resolution after rectification. The same size should be passed to
	/// [init_undistort_rectify_map] (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
	/// is passed (default), it is set to the original imageSize . Setting it to a larger value can help you
	/// preserve details in the original image, especially when there is a big radial distortion.
	/// * validPixROI1: Optional output rectangles inside the rectified images where all the pixels
	/// are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
	/// (see the picture below).
	/// * validPixROI2: Optional output rectangles inside the rectified images where all the pixels
	/// are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
	/// (see the picture below).
	///
	/// The function computes the rotation matrices for each camera that (virtually) make both camera image
	/// planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies
	/// the dense stereo correspondence problem. The function takes the matrices computed by [stereo_calibrate]
	/// as input. As output, it provides two rotation matrices and also two projection matrices in the new
	/// coordinates. The function distinguishes the following two cases:
	///
	/// *   **Horizontal stereo**: the first and the second camera views are shifted relative to each other
	///    mainly along the x-axis (with possible small vertical shift). In the rectified images, the
	///    corresponding epipolar lines in the left and right cameras are horizontal and have the same
	///    y-coordinate. P1 and P2 look like:
	///
	///    ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BP1%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20f%20%26%200%20%26%20cx%5F1%20%26%200%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%20f%20%26%20cy%20%26%200%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%201%20%26%200%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5Cend%7Bbmatrix%7D)
	///
	///    ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BP2%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20f%20%26%200%20%26%20cx%5F2%20%26%20T%5Fx%20%5Ccdot%20f%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%20f%20%26%20cy%20%26%200%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%201%20%26%200%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5Cend%7Bbmatrix%7D%20%2C)
	///
	///    ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BQ%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%201%20%26%200%20%26%200%20%26%20%2Dcx%5F1%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%201%20%26%200%20%26%20%2Dcy%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%200%20%26%20f%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%20%2D%5Cfrac%7B1%7D%7BT%5Fx%7D%20%26%20%5Cfrac%7Bcx%5F1%20%2D%20cx%5F2%7D%7BT%5Fx%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5Cend%7Bbmatrix%7D%20)
	///
	///    where ![inline formula](https://latex.codecogs.com/png.latex?T%5Fx) is a horizontal shift between the cameras and ![inline formula](https://latex.codecogs.com/png.latex?cx%5F1%3Dcx%5F2) if
	///    [CALIB_ZERO_DISPARITY] is set.
	///
	/// *   **Vertical stereo**: the first and the second camera views are shifted relative to each other
	///    mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar
	///    lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:
	///
	///    ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BP1%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20f%20%26%200%20%26%20cx%20%26%200%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%20f%20%26%20cy%5F1%20%26%200%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%201%20%26%200%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5Cend%7Bbmatrix%7D)
	///
	///    ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BP2%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20f%20%26%200%20%26%20cx%20%26%200%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%20f%20%26%20cy%5F2%20%26%20T%5Fy%20%5Ccdot%20f%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%201%20%26%200%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5Cend%7Bbmatrix%7D%2C)
	///
	///    ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BQ%7D%20%3D%20%5Cbegin%7Bbmatrix%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%201%20%26%200%20%26%200%20%26%20%2Dcx%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%201%20%26%200%20%26%20%2Dcy%5F1%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%200%20%26%20f%20%5C%5C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%20%26%200%20%26%20%2D%5Cfrac%7B1%7D%7BT%5Fy%7D%20%26%20%5Cfrac%7Bcy%5F1%20%2D%20cy%5F2%7D%7BT%5Fy%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5Cend%7Bbmatrix%7D%20)
	///
	///    where ![inline formula](https://latex.codecogs.com/png.latex?T%5Fy) is a vertical shift between the cameras and ![inline formula](https://latex.codecogs.com/png.latex?cy%5F1%3Dcy%5F2) if
	///    [CALIB_ZERO_DISPARITY] is set.
	///
	/// As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera
	/// matrices. The matrices, together with R1 and R2 , can then be passed to [init_undistort_rectify_map] to
	/// initialize the rectification map for each camera.
	///
	/// See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through
	/// the corresponding image regions. This means that the images are well rectified, which is what most
	/// stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that
	/// their interiors are all valid pixels.
	///
	/// ![image](https://docs.opencv.org/4.12.0/stereo_undistort.jpg)
	///
	/// ## C++ default parameters
	/// * flags: CALIB_ZERO_DISPARITY
	/// * alpha: -1
	/// * new_image_size: Size()
	/// * valid_pix_roi1: 0
	/// * valid_pix_roi2: 0
	#[inline]
	pub fn stereo_rectify(camera_matrix1: &impl ToInputArray, dist_coeffs1: &impl ToInputArray, camera_matrix2: &impl ToInputArray, dist_coeffs2: &impl ToInputArray, image_size: core::Size, r: &impl ToInputArray, t: &impl ToInputArray, r1: &mut impl ToOutputArray, r2: &mut impl ToOutputArray, p1: &mut impl ToOutputArray, p2: &mut impl ToOutputArray, q: &mut impl ToOutputArray, flags: i32, alpha: f64, new_image_size: core::Size, valid_pix_roi1: &mut core::Rect, valid_pix_roi2: &mut core::Rect) -> Result<()> {
		input_array_arg!(camera_matrix1);
		input_array_arg!(dist_coeffs1);
		input_array_arg!(camera_matrix2);
		input_array_arg!(dist_coeffs2);
		input_array_arg!(r);
		input_array_arg!(t);
		output_array_arg!(r1);
		output_array_arg!(r2);
		output_array_arg!(p1);
		output_array_arg!(p2);
		output_array_arg!(q);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_stereoRectify_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_Size_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_double_Size_RectX_RectX(camera_matrix1.as_raw__InputArray(), dist_coeffs1.as_raw__InputArray(), camera_matrix2.as_raw__InputArray(), dist_coeffs2.as_raw__InputArray(), &image_size, r.as_raw__InputArray(), t.as_raw__InputArray(), r1.as_raw__OutputArray(), r2.as_raw__OutputArray(), p1.as_raw__OutputArray(), p2.as_raw__OutputArray(), q.as_raw__OutputArray(), flags, alpha, &new_image_size, valid_pix_roi1, valid_pix_roi2, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// This function reconstructs 3-dimensional points (in homogeneous coordinates) by using
	/// their observations with a stereo camera.
	///
	/// ## Parameters
	/// * projMatr1: 3x4 projection matrix of the first camera, i.e. this matrix projects 3D points
	/// given in the world's coordinate system into the first image.
	/// * projMatr2: 3x4 projection matrix of the second camera, i.e. this matrix projects 3D points
	/// given in the world's coordinate system into the second image.
	/// * projPoints1: 2xN array of feature points in the first image. In the case of the c++ version,
	/// it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1.
	/// * projPoints2: 2xN array of corresponding points in the second image. In the case of the c++
	/// version, it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1.
	/// * points4D: 4xN array of reconstructed points in homogeneous coordinates. These points are
	/// returned in the world's coordinate system.
	///
	///
	/// Note:
	///    Keep in mind that all input data should be of float type in order for this function to work.
	///
	///
	/// Note:
	///    If the projection matrices from [stereoRectify] are used, then the returned points are
	///    represented in the first camera's rectified coordinate system.
	/// ## See also
	/// reprojectImageTo3D
	#[inline]
	pub fn triangulate_points(proj_matr1: &impl ToInputArray, proj_matr2: &impl ToInputArray, proj_points1: &impl ToInputArray, proj_points2: &impl ToInputArray, points4_d: &mut impl ToOutputArray) -> Result<()> {
		input_array_arg!(proj_matr1);
		input_array_arg!(proj_matr2);
		input_array_arg!(proj_points1);
		input_array_arg!(proj_points2);
		output_array_arg!(points4_d);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_triangulatePoints_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(proj_matr1.as_raw__InputArray(), proj_matr2.as_raw__InputArray(), proj_points1.as_raw__InputArray(), proj_points2.as_raw__InputArray(), points4_d.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Compute undistorted image points position
	///
	/// ## Parameters
	/// * src: Observed points position, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or
	/// CV_64FC2) (or vector\<Point2f\> ).
	/// * dst: Output undistorted points position (1xN/Nx1 2-channel or vector\<Point2f\> ).
	/// * cameraMatrix: Camera matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D) .
	/// * distCoeffs: Distortion coefficients
	///
	/// ## Note
	/// This alternative version of [undistort_image_points] function uses the following default values for its arguments:
	/// * unnamed: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,0.01)
	#[inline]
	pub fn undistort_image_points_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray) -> Result<()> {
		input_array_arg!(src);
		output_array_arg!(dst);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_undistortImagePoints_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Compute undistorted image points position
	///
	/// ## Parameters
	/// * src: Observed points position, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or
	/// CV_64FC2) (or vector\<Point2f\> ).
	/// * dst: Output undistorted points position (1xN/Nx1 2-channel or vector\<Point2f\> ).
	/// * cameraMatrix: Camera matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D) .
	/// * distCoeffs: Distortion coefficients
	///
	/// ## C++ default parameters
	/// * unnamed: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,0.01)
	#[inline]
	pub fn undistort_image_points(src: &impl ToInputArray, dst: &mut impl ToOutputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, unnamed: core::TermCriteria) -> Result<()> {
		input_array_arg!(src);
		output_array_arg!(dst);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_undistortImagePoints_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_TermCriteria(src.as_raw__InputArray(), dst.as_raw__OutputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), &unnamed, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes the ideal point coordinates from the observed point coordinates.
	///
	/// The function is similar to [undistort] and [init_undistort_rectify_map] but it operates on a
	/// sparse set of points instead of a raster image. Also the function performs a reverse transformation
	/// to  #projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a
	/// planar object, it does, up to a translation vector, if the proper R is specified.
	///
	/// For each observed point coordinate ![inline formula](https://latex.codecogs.com/png.latex?%28u%2C%20v%29) the function computes:
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Barray%7D%7Bl%7D%0Ax%5E%7B%22%7D%20%20%5Cleftarrow%20%28u%20%2D%20c%5Fx%29%2Ff%5Fx%20%20%5C%5C%0Ay%5E%7B%22%7D%20%20%5Cleftarrow%20%28v%20%2D%20c%5Fy%29%2Ff%5Fy%20%20%5C%5C%0A%28x%27%2Cy%27%29%20%3D%20undistort%28x%5E%7B%22%7D%2Cy%5E%7B%22%7D%2C%20%5Ctexttt%7BdistCoeffs%7D%29%20%5C%5C%0A%7B%5BX%5C%2CY%5C%2CW%5D%7D%20%5ET%20%20%5Cleftarrow%20R%2A%5Bx%27%20%5C%2C%20y%27%20%5C%2C%201%5D%5ET%20%20%5C%5C%0Ax%20%20%5Cleftarrow%20X%2FW%20%20%5C%5C%0Ay%20%20%5Cleftarrow%20Y%2FW%20%20%5C%5C%0A%5Ctext%7Bonly%20performed%20if%20P%20is%20specified%3A%7D%20%5C%5C%0Au%27%20%20%5Cleftarrow%20x%20%7Bf%27%7D%5Fx%20%2B%20%7Bc%27%7D%5Fx%20%20%5C%5C%0Av%27%20%20%5Cleftarrow%20y%20%7Bf%27%7D%5Fy%20%2B%20%7Bc%27%7D%5Fy%0A%5Cend%7Barray%7D%0A)
	///
	/// where *undistort* is an approximate iterative algorithm that estimates the normalized original
	/// point coordinates out of the normalized distorted point coordinates ("normalized" means that the
	/// coordinates do not depend on the camera matrix).
	///
	/// The function can be used for both a stereo camera head or a monocular camera (when R is empty).
	/// ## Parameters
	/// * src: Observed point coordinates, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or
	/// vector\<Point2f\> ).
	/// * dst: Output ideal point coordinates (1xN/Nx1 2-channel or vector\<Point2f\> ) after undistortion and reverse perspective
	/// transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
	/// * cameraMatrix: Camera matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%28k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%5B%2C%20k%5F3%5B%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%5B%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%20s%5F4%5B%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%5D%5D%5D%5D%29)
	/// of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
	/// * R: Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by
	/// [stereo_rectify] can be passed here. If the matrix is empty, the identity transformation is used.
	/// * P: New camera matrix (3x3) or new projection matrix (3x4) ![inline formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20%7Bf%27%7D%5Fx%20%26%200%20%26%20%7Bc%27%7D%5Fx%20%26%20t%5Fx%20%5C%5C%200%20%26%20%7Bf%27%7D%5Fy%20%26%20%7Bc%27%7D%5Fy%20%26%20t%5Fy%20%5C%5C%200%20%26%200%20%26%201%20%26%20t%5Fz%20%5Cend%7Bbmatrix%7D). P1 or P2 computed by
	/// [stereo_rectify] can be passed here. If the matrix is empty, the identity new camera matrix is used.
	///
	/// ## Note
	/// This alternative version of [undistort_points] function uses the following default values for its arguments:
	/// * r: noArray()
	/// * p: noArray()
	#[inline]
	pub fn undistort_points_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray) -> Result<()> {
		input_array_arg!(src);
		output_array_arg!(dst);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_undistortPoints_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes the ideal point coordinates from the observed point coordinates.
	///
	/// The function is similar to [undistort] and [init_undistort_rectify_map] but it operates on a
	/// sparse set of points instead of a raster image. Also the function performs a reverse transformation
	/// to  #projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a
	/// planar object, it does, up to a translation vector, if the proper R is specified.
	///
	/// For each observed point coordinate ![inline formula](https://latex.codecogs.com/png.latex?%28u%2C%20v%29) the function computes:
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Barray%7D%7Bl%7D%0Ax%5E%7B%22%7D%20%20%5Cleftarrow%20%28u%20%2D%20c%5Fx%29%2Ff%5Fx%20%20%5C%5C%0Ay%5E%7B%22%7D%20%20%5Cleftarrow%20%28v%20%2D%20c%5Fy%29%2Ff%5Fy%20%20%5C%5C%0A%28x%27%2Cy%27%29%20%3D%20undistort%28x%5E%7B%22%7D%2Cy%5E%7B%22%7D%2C%20%5Ctexttt%7BdistCoeffs%7D%29%20%5C%5C%0A%7B%5BX%5C%2CY%5C%2CW%5D%7D%20%5ET%20%20%5Cleftarrow%20R%2A%5Bx%27%20%5C%2C%20y%27%20%5C%2C%201%5D%5ET%20%20%5C%5C%0Ax%20%20%5Cleftarrow%20X%2FW%20%20%5C%5C%0Ay%20%20%5Cleftarrow%20Y%2FW%20%20%5C%5C%0A%5Ctext%7Bonly%20performed%20if%20P%20is%20specified%3A%7D%20%5C%5C%0Au%27%20%20%5Cleftarrow%20x%20%7Bf%27%7D%5Fx%20%2B%20%7Bc%27%7D%5Fx%20%20%5C%5C%0Av%27%20%20%5Cleftarrow%20y%20%7Bf%27%7D%5Fy%20%2B%20%7Bc%27%7D%5Fy%0A%5Cend%7Barray%7D%0A)
	///
	/// where *undistort* is an approximate iterative algorithm that estimates the normalized original
	/// point coordinates out of the normalized distorted point coordinates ("normalized" means that the
	/// coordinates do not depend on the camera matrix).
	///
	/// The function can be used for both a stereo camera head or a monocular camera (when R is empty).
	/// ## Parameters
	/// * src: Observed point coordinates, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or
	/// vector\<Point2f\> ).
	/// * dst: Output ideal point coordinates (1xN/Nx1 2-channel or vector\<Point2f\> ) after undistortion and reverse perspective
	/// transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
	/// * cameraMatrix: Camera matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%28k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%5B%2C%20k%5F3%5B%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%5B%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%20s%5F4%5B%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%5D%5D%5D%5D%29)
	/// of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
	/// * R: Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by
	/// [stereo_rectify] can be passed here. If the matrix is empty, the identity transformation is used.
	/// * P: New camera matrix (3x3) or new projection matrix (3x4) ![inline formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20%7Bf%27%7D%5Fx%20%26%200%20%26%20%7Bc%27%7D%5Fx%20%26%20t%5Fx%20%5C%5C%200%20%26%20%7Bf%27%7D%5Fy%20%26%20%7Bc%27%7D%5Fy%20%26%20t%5Fy%20%5C%5C%200%20%26%200%20%26%201%20%26%20t%5Fz%20%5Cend%7Bbmatrix%7D). P1 or P2 computed by
	/// [stereo_rectify] can be passed here. If the matrix is empty, the identity new camera matrix is used.
	///
	/// ## C++ default parameters
	/// * r: noArray()
	/// * p: noArray()
	#[inline]
	pub fn undistort_points(src: &impl ToInputArray, dst: &mut impl ToOutputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, r: &impl ToInputArray, p: &impl ToInputArray) -> Result<()> {
		input_array_arg!(src);
		output_array_arg!(dst);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		input_array_arg!(r);
		input_array_arg!(p);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_undistortPoints_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), r.as_raw__InputArray(), p.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Computes the ideal point coordinates from the observed point coordinates.
	///
	/// The function is similar to [undistort] and [init_undistort_rectify_map] but it operates on a
	/// sparse set of points instead of a raster image. Also the function performs a reverse transformation
	/// to  #projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a
	/// planar object, it does, up to a translation vector, if the proper R is specified.
	///
	/// For each observed point coordinate ![inline formula](https://latex.codecogs.com/png.latex?%28u%2C%20v%29) the function computes:
	/// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Barray%7D%7Bl%7D%0Ax%5E%7B%22%7D%20%20%5Cleftarrow%20%28u%20%2D%20c%5Fx%29%2Ff%5Fx%20%20%5C%5C%0Ay%5E%7B%22%7D%20%20%5Cleftarrow%20%28v%20%2D%20c%5Fy%29%2Ff%5Fy%20%20%5C%5C%0A%28x%27%2Cy%27%29%20%3D%20undistort%28x%5E%7B%22%7D%2Cy%5E%7B%22%7D%2C%20%5Ctexttt%7BdistCoeffs%7D%29%20%5C%5C%0A%7B%5BX%5C%2CY%5C%2CW%5D%7D%20%5ET%20%20%5Cleftarrow%20R%2A%5Bx%27%20%5C%2C%20y%27%20%5C%2C%201%5D%5ET%20%20%5C%5C%0Ax%20%20%5Cleftarrow%20X%2FW%20%20%5C%5C%0Ay%20%20%5Cleftarrow%20Y%2FW%20%20%5C%5C%0A%5Ctext%7Bonly%20performed%20if%20P%20is%20specified%3A%7D%20%5C%5C%0Au%27%20%20%5Cleftarrow%20x%20%7Bf%27%7D%5Fx%20%2B%20%7Bc%27%7D%5Fx%20%20%5C%5C%0Av%27%20%20%5Cleftarrow%20y%20%7Bf%27%7D%5Fy%20%2B%20%7Bc%27%7D%5Fy%0A%5Cend%7Barray%7D%0A)
	///
	/// where *undistort* is an approximate iterative algorithm that estimates the normalized original
	/// point coordinates out of the normalized distorted point coordinates ("normalized" means that the
	/// coordinates do not depend on the camera matrix).
	///
	/// The function can be used for both a stereo camera head or a monocular camera (when R is empty).
	/// ## Parameters
	/// * src: Observed point coordinates, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or
	/// vector\<Point2f\> ).
	/// * dst: Output ideal point coordinates (1xN/Nx1 2-channel or vector\<Point2f\> ) after undistortion and reverse perspective
	/// transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
	/// * cameraMatrix: Camera matrix ![inline formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%28k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%5B%2C%20k%5F3%5B%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%5B%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%20s%5F4%5B%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%5D%5D%5D%5D%29)
	/// of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
	/// * R: Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by
	/// [stereo_rectify] can be passed here. If the matrix is empty, the identity transformation is used.
	/// * P: New camera matrix (3x3) or new projection matrix (3x4) ![inline formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20%7Bf%27%7D%5Fx%20%26%200%20%26%20%7Bc%27%7D%5Fx%20%26%20t%5Fx%20%5C%5C%200%20%26%20%7Bf%27%7D%5Fy%20%26%20%7Bc%27%7D%5Fy%20%26%20t%5Fy%20%5C%5C%200%20%26%200%20%26%201%20%26%20t%5Fz%20%5Cend%7Bbmatrix%7D). P1 or P2 computed by
	/// [stereo_rectify] can be passed here. If the matrix is empty, the identity new camera matrix is used.
	///
	/// ## Overloaded parameters
	///
	///
	/// Note: Default version of [undistort_points] does 5 iterations to compute undistorted points.
	#[inline]
	pub fn undistort_points_iter(src: &impl ToInputArray, dst: &mut impl ToOutputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, r: &impl ToInputArray, p: &impl ToInputArray, criteria: core::TermCriteria) -> Result<()> {
		input_array_arg!(src);
		output_array_arg!(dst);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		input_array_arg!(r);
		input_array_arg!(p);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_undistortPoints_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_TermCriteria(src.as_raw__InputArray(), dst.as_raw__OutputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), r.as_raw__InputArray(), p.as_raw__InputArray(), &criteria, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Transforms an image to compensate for lens distortion.
	///
	/// The function transforms an image to compensate radial and tangential lens distortion.
	///
	/// The function is simply a combination of [init_undistort_rectify_map] (with unity R ) and [remap]
	/// (with bilinear interpolation). See the former function for details of the transformation being
	/// performed.
	///
	/// Those pixels in the destination image, for which there is no correspondent pixels in the source
	/// image, are filled with zeros (black color).
	///
	/// A particular subset of the source image that will be visible in the corrected image can be regulated
	/// by newCameraMatrix. You can use [get_optimal_new_camera_matrix] to compute the appropriate
	/// newCameraMatrix depending on your requirements.
	///
	/// The camera matrix and the distortion parameters can be determined using #calibrateCamera. If
	/// the resolution of images is different from the resolution used at the calibration stage, ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx%2C%0Af%5Fy%2C%20c%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?c%5Fy) need to be scaled accordingly, while the distortion coefficients remain
	/// the same.
	///
	/// ## Parameters
	/// * src: Input (distorted) image.
	/// * dst: Output (corrected) image that has the same size and type as src .
	/// * cameraMatrix: Input camera matrix ![inline formula](https://latex.codecogs.com/png.latex?A%20%3D%20%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%28k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%5B%2C%20k%5F3%5B%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%5B%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%20s%5F4%5B%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%5D%5D%5D%5D%29)
	/// of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
	/// * newCameraMatrix: Camera matrix of the distorted image. By default, it is the same as
	/// cameraMatrix but you may additionally scale and shift the result by using a different matrix.
	///
	/// ## Note
	/// This alternative version of [undistort] function uses the following default values for its arguments:
	/// * new_camera_matrix: noArray()
	#[inline]
	pub fn undistort_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray) -> Result<()> {
		input_array_arg!(src);
		output_array_arg!(dst);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_undistort_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// Transforms an image to compensate for lens distortion.
	///
	/// The function transforms an image to compensate radial and tangential lens distortion.
	///
	/// The function is simply a combination of [init_undistort_rectify_map] (with unity R ) and [remap]
	/// (with bilinear interpolation). See the former function for details of the transformation being
	/// performed.
	///
	/// Those pixels in the destination image, for which there is no correspondent pixels in the source
	/// image, are filled with zeros (black color).
	///
	/// A particular subset of the source image that will be visible in the corrected image can be regulated
	/// by newCameraMatrix. You can use [get_optimal_new_camera_matrix] to compute the appropriate
	/// newCameraMatrix depending on your requirements.
	///
	/// The camera matrix and the distortion parameters can be determined using #calibrateCamera. If
	/// the resolution of images is different from the resolution used at the calibration stage, ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx%2C%0Af%5Fy%2C%20c%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?c%5Fy) need to be scaled accordingly, while the distortion coefficients remain
	/// the same.
	///
	/// ## Parameters
	/// * src: Input (distorted) image.
	/// * dst: Output (corrected) image that has the same size and type as src .
	/// * cameraMatrix: Input camera matrix ![inline formula](https://latex.codecogs.com/png.latex?A%20%3D%20%5Cbegin%7Bbmatrix%7D%20f%5Fx%20%26%200%20%26%20c%5Fx%5C%5C%200%20%26%20f%5Fy%20%26%20c%5Fy%5C%5C%200%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D) .
	/// * distCoeffs: Input vector of distortion coefficients
	/// ![inline formula](https://latex.codecogs.com/png.latex?%28k%5F1%2C%20k%5F2%2C%20p%5F1%2C%20p%5F2%5B%2C%20k%5F3%5B%2C%20k%5F4%2C%20k%5F5%2C%20k%5F6%5B%2C%20s%5F1%2C%20s%5F2%2C%20s%5F3%2C%20s%5F4%5B%2C%20%5Ctau%5Fx%2C%20%5Ctau%5Fy%5D%5D%5D%5D%29)
	/// of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
	/// * newCameraMatrix: Camera matrix of the distorted image. By default, it is the same as
	/// cameraMatrix but you may additionally scale and shift the result by using a different matrix.
	///
	/// ## C++ default parameters
	/// * new_camera_matrix: noArray()
	#[inline]
	pub fn undistort(src: &impl ToInputArray, dst: &mut impl ToOutputArray, camera_matrix: &impl ToInputArray, dist_coeffs: &impl ToInputArray, new_camera_matrix: &impl ToInputArray) -> Result<()> {
		input_array_arg!(src);
		output_array_arg!(dst);
		input_array_arg!(camera_matrix);
		input_array_arg!(dist_coeffs);
		input_array_arg!(new_camera_matrix);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_undistort_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), camera_matrix.as_raw__InputArray(), dist_coeffs.as_raw__InputArray(), new_camera_matrix.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// validates disparity using the left-right check. The matrix "cost" should be computed by the stereo correspondence algorithm
	///
	/// ## Note
	/// This alternative version of [validate_disparity] function uses the following default values for its arguments:
	/// * disp12_max_disp: 1
	#[inline]
	pub fn validate_disparity_def(disparity: &mut impl ToInputOutputArray, cost: &impl ToInputArray, min_disparity: i32, number_of_disparities: i32) -> Result<()> {
		input_output_array_arg!(disparity);
		input_array_arg!(cost);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_validateDisparity_const__InputOutputArrayR_const__InputArrayR_int_int(disparity.as_raw__InputOutputArray(), cost.as_raw__InputArray(), min_disparity, number_of_disparities, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	/// validates disparity using the left-right check. The matrix "cost" should be computed by the stereo correspondence algorithm
	///
	/// ## C++ default parameters
	/// * disp12_max_disp: 1
	#[inline]
	pub fn validate_disparity(disparity: &mut impl ToInputOutputArray, cost: &impl ToInputArray, min_disparity: i32, number_of_disparities: i32, disp12_max_disp: i32) -> Result<()> {
		input_output_array_arg!(disparity);
		input_array_arg!(cost);
		return_send!(via ocvrs_return);
		unsafe { sys::cv_validateDisparity_const__InputOutputArrayR_const__InputArrayR_int_int_int(disparity.as_raw__InputOutputArray(), cost.as_raw__InputArray(), min_disparity, number_of_disparities, disp12_max_disp, ocvrs_return.as_mut_ptr()) };
		return_receive!(ocvrs_return => ret);
		let ret = ret.into_result()?;
		Ok(ret)
	}

	#[repr(C)]
	#[derive(Copy, Clone, Debug, PartialEq)]
	pub struct CirclesGridFinderParameters {
		pub density_neighborhood_size: core::Size2f,
		pub min_density: f32,
		pub kmeans_attempts: i32,
		pub min_distance_to_add_keypoint: i32,
		pub keypoint_scale: i32,
		pub min_graph_confidence: f32,
		pub vertex_gain: f32,
		pub vertex_penalty: f32,
		pub existing_vertex_gain: f32,
		pub edge_gain: f32,
		pub edge_penalty: f32,
		pub convex_hull_factor: f32,
		pub min_rng_edge_switch_dist: f32,
		pub grid_type: crate::calib3d::CirclesGridFinderParameters_GridType,
		/// Distance between two adjacent points. Used by CALIB_CB_CLUSTERING.
		pub square_size: f32,
		/// Max deviation from prediction. Used by CALIB_CB_CLUSTERING.
		pub max_rectified_distance: f32,
	}

	opencv_type_simple! { crate::calib3d::CirclesGridFinderParameters }

	impl CirclesGridFinderParameters {
		#[inline]
		pub fn default() -> Result<crate::calib3d::CirclesGridFinderParameters> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_CirclesGridFinderParameters_CirclesGridFinderParameters(ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

	}

	/// Levenberg-Marquardt solver. Starting with the specified vector of parameters it
	/// optimizes the target vector criteria "err"
	/// (finds local minima of each target vector component absolute value).
	///
	/// When needed, it calls user-provided callback.
	pub struct LMSolver {
		ptr: *mut c_void,
	}

	opencv_type_boxed! { LMSolver }

	impl Drop for LMSolver {
		#[inline]
		fn drop(&mut self) {
			unsafe { sys::cv_LMSolver_delete(self.as_raw_mut_LMSolver()) };
		}
	}

	unsafe impl Send for LMSolver {}

	impl LMSolver {
		/// Creates Levenberg-Marquard solver
		///
		/// ## Parameters
		/// * cb: callback
		/// * maxIters: maximum number of iterations that can be further
		///   modified using setMaxIters() method.
		#[inline]
		pub fn create(cb: &core::Ptr<crate::calib3d::LMSolver_Callback>, max_iters: i32) -> Result<core::Ptr<crate::calib3d::LMSolver>> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_LMSolver_create_const_PtrLCallbackGR_int(cb.as_raw_PtrOfLMSolver_Callback(), max_iters, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			let ret = unsafe { core::Ptr::<crate::calib3d::LMSolver>::opencv_from_extern(ret) };
			Ok(ret)
		}

		#[inline]
		pub fn create_ext(cb: &core::Ptr<crate::calib3d::LMSolver_Callback>, max_iters: i32, eps: f64) -> Result<core::Ptr<crate::calib3d::LMSolver>> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_LMSolver_create_const_PtrLCallbackGR_int_double(cb.as_raw_PtrOfLMSolver_Callback(), max_iters, eps, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			let ret = unsafe { core::Ptr::<crate::calib3d::LMSolver>::opencv_from_extern(ret) };
			Ok(ret)
		}

	}

	/// Constant methods for [crate::calib3d::LMSolver]
	pub trait LMSolverTraitConst: core::AlgorithmTraitConst {
		fn as_raw_LMSolver(&self) -> *const c_void;

		/// Runs Levenberg-Marquardt algorithm using the passed vector of parameters as the start point.
		/// The final vector of parameters (whether the algorithm converged or not) is stored at the same
		/// vector. The method returns the number of iterations used. If it's equal to the previously specified
		/// maxIters, there is a big chance the algorithm did not converge.
		///
		/// ## Parameters
		/// * param: initial/final vector of parameters.
		///
		/// Note that the dimensionality of parameter space is defined by the size of param vector,
		/// and the dimensionality of optimized criteria is defined by the size of err vector
		/// computed by the callback.
		#[inline]
		fn run(&self, param: &mut impl ToInputOutputArray) -> Result<i32> {
			input_output_array_arg!(param);
			return_send!(via ocvrs_return);
			unsafe { sys::cv_LMSolver_run_const_const__InputOutputArrayR(self.as_raw_LMSolver(), param.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Retrieves the current maximum number of iterations
		#[inline]
		fn get_max_iters(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_LMSolver_getMaxIters_const(self.as_raw_LMSolver(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

	}

	/// Mutable methods for [crate::calib3d::LMSolver]
	pub trait LMSolverTrait: core::AlgorithmTrait + crate::calib3d::LMSolverTraitConst {
		fn as_raw_mut_LMSolver(&mut self) -> *mut c_void;

		/// Sets the maximum number of iterations
		/// ## Parameters
		/// * maxIters: the number of iterations
		#[inline]
		fn set_max_iters(&mut self, max_iters: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_LMSolver_setMaxIters_int(self.as_raw_mut_LMSolver(), max_iters, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

	}

	impl std::fmt::Debug for LMSolver {
		#[inline]
		fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
			f.debug_struct("LMSolver")
				.finish()
		}
	}

	boxed_cast_base! { LMSolver, core::Algorithm, cv_LMSolver_to_Algorithm }

	impl core::AlgorithmTraitConst for LMSolver {
		#[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() }
	}

	impl core::AlgorithmTrait for LMSolver {
		#[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() }
	}

	boxed_ref! { LMSolver, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm }

	impl crate::calib3d::LMSolverTraitConst for LMSolver {
		#[inline] fn as_raw_LMSolver(&self) -> *const c_void { self.as_raw() }
	}

	impl crate::calib3d::LMSolverTrait for LMSolver {
		#[inline] fn as_raw_mut_LMSolver(&mut self) -> *mut c_void { self.as_raw_mut() }
	}

	boxed_ref! { LMSolver, crate::calib3d::LMSolverTraitConst, as_raw_LMSolver, crate::calib3d::LMSolverTrait, as_raw_mut_LMSolver }

	pub struct LMSolver_Callback {
		ptr: *mut c_void,
	}

	opencv_type_boxed! { LMSolver_Callback }

	impl Drop for LMSolver_Callback {
		#[inline]
		fn drop(&mut self) {
			unsafe { sys::cv_LMSolver_Callback_delete(self.as_raw_mut_LMSolver_Callback()) };
		}
	}

	unsafe impl Send for LMSolver_Callback {}

	/// Constant methods for [crate::calib3d::LMSolver_Callback]
	pub trait LMSolver_CallbackTraitConst {
		fn as_raw_LMSolver_Callback(&self) -> *const c_void;

		/// computes error and Jacobian for the specified vector of parameters
		///
		/// ## Parameters
		/// * param: the current vector of parameters
		/// * err: output vector of errors: err_i = actual_f_i - ideal_f_i
		/// * J: output Jacobian: J_ij = d(ideal_f_i)/d(param_j)
		///
		/// when J=noArray(), it means that it does not need to be computed.
		/// Dimensionality of error vector and param vector can be different.
		/// The callback should explicitly allocate (with "create" method) each output array
		/// (unless it's noArray()).
		#[inline]
		fn compute(&self, param: &impl ToInputArray, err: &mut impl ToOutputArray, j: &mut impl ToOutputArray) -> Result<bool> {
			input_array_arg!(param);
			output_array_arg!(err);
			output_array_arg!(j);
			return_send!(via ocvrs_return);
			unsafe { sys::cv_LMSolver_Callback_compute_const_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(self.as_raw_LMSolver_Callback(), param.as_raw__InputArray(), err.as_raw__OutputArray(), j.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

	}

	/// Mutable methods for [crate::calib3d::LMSolver_Callback]
	pub trait LMSolver_CallbackTrait: crate::calib3d::LMSolver_CallbackTraitConst {
		fn as_raw_mut_LMSolver_Callback(&mut self) -> *mut c_void;

	}

	impl std::fmt::Debug for LMSolver_Callback {
		#[inline]
		fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
			f.debug_struct("LMSolver_Callback")
				.finish()
		}
	}

	impl crate::calib3d::LMSolver_CallbackTraitConst for LMSolver_Callback {
		#[inline] fn as_raw_LMSolver_Callback(&self) -> *const c_void { self.as_raw() }
	}

	impl crate::calib3d::LMSolver_CallbackTrait for LMSolver_Callback {
		#[inline] fn as_raw_mut_LMSolver_Callback(&mut self) -> *mut c_void { self.as_raw_mut() }
	}

	boxed_ref! { LMSolver_Callback, crate::calib3d::LMSolver_CallbackTraitConst, as_raw_LMSolver_Callback, crate::calib3d::LMSolver_CallbackTrait, as_raw_mut_LMSolver_Callback }

	/// Class for computing stereo correspondence using the block matching algorithm, introduced and contributed to OpenCV by K. Konolige.
	/// @details This class implements a block matching algorithm for stereo correspondence, which is used to compute disparity maps from stereo image pairs. It provides methods to fine-tune parameters such as pre-filtering, texture thresholds, uniqueness ratios, and regions of interest (ROIs) to optimize performance and accuracy.
	pub struct StereoBM {
		ptr: *mut c_void,
	}

	opencv_type_boxed! { StereoBM }

	impl Drop for StereoBM {
		#[inline]
		fn drop(&mut self) {
			unsafe { sys::cv_StereoBM_delete(self.as_raw_mut_StereoBM()) };
		}
	}

	unsafe impl Send for StereoBM {}

	impl StereoBM {
		/// Creates StereoBM object
		/// ## Parameters
		/// * numDisparities: The disparity search range. For each pixel, the algorithm will find the best disparity from 0 (default minimum disparity) to numDisparities. The search range can be shifted by changing the minimum disparity.
		/// * blockSize: The linear size of the blocks compared by the algorithm. The size should be odd (as the block is centered at the current pixel). Larger block size implies smoother, though less accurate disparity map. Smaller block size gives more detailed disparity map, but there is a higher chance for the algorithm to find a wrong correspondence.
		/// ## Returns
		/// A pointer to the created StereoBM object.
		/// @details The function creates a StereoBM object. You can then call StereoBM::compute() to compute disparity for a specific stereo pair.
		///
		/// ## C++ default parameters
		/// * num_disparities: 0
		/// * block_size: 21
		#[inline]
		pub fn create(num_disparities: i32, block_size: i32) -> Result<core::Ptr<crate::calib3d::StereoBM>> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_create_int_int(num_disparities, block_size, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			let ret = unsafe { core::Ptr::<crate::calib3d::StereoBM>::opencv_from_extern(ret) };
			Ok(ret)
		}

		/// Creates StereoBM object
		/// ## Parameters
		/// * numDisparities: The disparity search range. For each pixel, the algorithm will find the best disparity from 0 (default minimum disparity) to numDisparities. The search range can be shifted by changing the minimum disparity.
		/// * blockSize: The linear size of the blocks compared by the algorithm. The size should be odd (as the block is centered at the current pixel). Larger block size implies smoother, though less accurate disparity map. Smaller block size gives more detailed disparity map, but there is a higher chance for the algorithm to find a wrong correspondence.
		/// ## Returns
		/// A pointer to the created StereoBM object.
		/// @details The function creates a StereoBM object. You can then call StereoBM::compute() to compute disparity for a specific stereo pair.
		///
		/// ## Note
		/// This alternative version of [StereoBM::create] function uses the following default values for its arguments:
		/// * num_disparities: 0
		/// * block_size: 21
		#[inline]
		pub fn create_def() -> Result<core::Ptr<crate::calib3d::StereoBM>> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_create(ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			let ret = unsafe { core::Ptr::<crate::calib3d::StereoBM>::opencv_from_extern(ret) };
			Ok(ret)
		}

	}

	/// Constant methods for [crate::calib3d::StereoBM]
	pub trait StereoBMTraitConst: crate::calib3d::StereoMatcherTraitConst {
		fn as_raw_StereoBM(&self) -> *const c_void;

		/// Gets the type of pre-filtering currently used in the algorithm.
		/// ## Returns
		/// The current pre-filter type: 0 for PREFILTER_NORMALIZED_RESPONSE or 1 for PREFILTER_XSOBEL.
		#[inline]
		fn get_pre_filter_type(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_getPreFilterType_const(self.as_raw_StereoBM(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Gets the current size of the pre-filter kernel.
		/// ## Returns
		/// The current pre-filter size.
		#[inline]
		fn get_pre_filter_size(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_getPreFilterSize_const(self.as_raw_StereoBM(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Gets the current truncation value for prefiltered pixels.
		/// ## Returns
		/// The current pre-filter cap value.
		#[inline]
		fn get_pre_filter_cap(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_getPreFilterCap_const(self.as_raw_StereoBM(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Gets the current texture threshold value.
		/// ## Returns
		/// The current texture threshold.
		#[inline]
		fn get_texture_threshold(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_getTextureThreshold_const(self.as_raw_StereoBM(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Gets the current uniqueness ratio value.
		/// ## Returns
		/// The current uniqueness ratio.
		#[inline]
		fn get_uniqueness_ratio(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_getUniquenessRatio_const(self.as_raw_StereoBM(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Gets the current size of the smaller block used for texture check.
		/// ## Returns
		/// The current smaller block size.
		#[inline]
		fn get_smaller_block_size(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_getSmallerBlockSize_const(self.as_raw_StereoBM(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Gets the current Region of Interest (ROI) for the left image.
		/// ## Returns
		/// The current ROI for the left image.
		#[inline]
		fn get_roi1(&self) -> Result<core::Rect> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_getROI1_const(self.as_raw_StereoBM(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Gets the current Region of Interest (ROI) for the right image.
		/// ## Returns
		/// The current ROI for the right image.
		#[inline]
		fn get_roi2(&self) -> Result<core::Rect> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_getROI2_const(self.as_raw_StereoBM(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

	}

	/// Mutable methods for [crate::calib3d::StereoBM]
	pub trait StereoBMTrait: crate::calib3d::StereoBMTraitConst + crate::calib3d::StereoMatcherTrait {
		fn as_raw_mut_StereoBM(&mut self) -> *mut c_void;

		/// Sets the type of pre-filtering used in the algorithm.
		/// ## Parameters
		/// * preFilterType: The type of pre-filter to use. Possible values are:
		/// - PREFILTER_NORMALIZED_RESPONSE (0): Uses normalized response for pre-filtering.
		/// - PREFILTER_XSOBEL (1): Uses the X-Sobel operator for pre-filtering.
		/// @details The pre-filter type affects how the images are prepared before computing the disparity map. Different pre-filtering methods can enhance specific image features or reduce noise, influencing the quality of the disparity map.
		#[inline]
		fn set_pre_filter_type(&mut self, pre_filter_type: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_setPreFilterType_int(self.as_raw_mut_StereoBM(), pre_filter_type, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Sets the size of the pre-filter kernel.
		/// ## Parameters
		/// * preFilterSize: The size of the pre-filter kernel. Must be an odd integer, typically between 5 and 255.
		/// @details The pre-filter size determines the spatial extent of the pre-filtering operation, which prepares the images for disparity computation by normalizing brightness and enhancing texture. Larger sizes reduce noise but may blur details, while smaller sizes preserve details but are more susceptible to noise.
		#[inline]
		fn set_pre_filter_size(&mut self, pre_filter_size: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_setPreFilterSize_int(self.as_raw_mut_StereoBM(), pre_filter_size, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Sets the truncation value for prefiltered pixels.
		/// ## Parameters
		/// * preFilterCap: The truncation value. Typically in the range [1, 63].
		/// @details This value caps the output of the pre-filter to [-preFilterCap, preFilterCap], helping to reduce the impact of noise and outliers in the pre-filtered image.
		#[inline]
		fn set_pre_filter_cap(&mut self, pre_filter_cap: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_setPreFilterCap_int(self.as_raw_mut_StereoBM(), pre_filter_cap, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Sets the threshold for filtering low-texture regions.
		/// ## Parameters
		/// * textureThreshold: The threshold value. Must be non-negative.
		/// @details This parameter filters out regions with low texture, where establishing correspondences is difficult, thus reducing noise in the disparity map. Higher values filter more aggressively but may discard valid information.
		#[inline]
		fn set_texture_threshold(&mut self, texture_threshold: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_setTextureThreshold_int(self.as_raw_mut_StereoBM(), texture_threshold, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Sets the uniqueness ratio for filtering ambiguous matches.
		/// ## Parameters
		/// * uniquenessRatio: The uniqueness ratio value. Typically in the range [5, 15], but can be from 0 to 100.
		/// @details This parameter ensures that the best match is sufficiently better than the next best match, reducing false positives. Higher values are stricter but may filter out valid matches in difficult regions.
		#[inline]
		fn set_uniqueness_ratio(&mut self, uniqueness_ratio: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_setUniquenessRatio_int(self.as_raw_mut_StereoBM(), uniqueness_ratio, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Sets the size of the smaller block used for texture check.
		/// ## Parameters
		/// * blockSize: The size of the smaller block. Must be an odd integer between 5 and 255.
		/// @details This parameter determines the size of the block used to compute texture variance. Smaller blocks capture finer details but are more sensitive to noise, while larger blocks are more robust but may miss fine details.
		#[inline]
		fn set_smaller_block_size(&mut self, block_size: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_setSmallerBlockSize_int(self.as_raw_mut_StereoBM(), block_size, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Sets the Region of Interest (ROI) for the left image.
		/// ## Parameters
		/// * roi1: The ROI rectangle for the left image.
		/// @details By setting the ROI, the stereo matching computation is limited to the specified region, improving performance and potentially accuracy by focusing on relevant parts of the image.
		#[inline]
		fn set_roi1(&mut self, roi1: core::Rect) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_setROI1_Rect(self.as_raw_mut_StereoBM(), &roi1, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		/// Sets the Region of Interest (ROI) for the right image.
		/// ## Parameters
		/// * roi2: The ROI rectangle for the right image.
		/// @details Similar to setROI1, this limits the computation to the specified region in the right image.
		#[inline]
		fn set_roi2(&mut self, roi2: core::Rect) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoBM_setROI2_Rect(self.as_raw_mut_StereoBM(), &roi2, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

	}

	impl std::fmt::Debug for StereoBM {
		#[inline]
		fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
			f.debug_struct("StereoBM")
				.finish()
		}
	}

	boxed_cast_base! { StereoBM, core::Algorithm, cv_StereoBM_to_Algorithm }

	boxed_cast_base! { StereoBM, crate::calib3d::StereoMatcher, cv_StereoBM_to_StereoMatcher }

	impl core::AlgorithmTraitConst for StereoBM {
		#[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() }
	}

	impl core::AlgorithmTrait for StereoBM {
		#[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() }
	}

	boxed_ref! { StereoBM, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm }

	impl crate::calib3d::StereoMatcherTraitConst for StereoBM {
		#[inline] fn as_raw_StereoMatcher(&self) -> *const c_void { self.as_raw() }
	}

	impl crate::calib3d::StereoMatcherTrait for StereoBM {
		#[inline] fn as_raw_mut_StereoMatcher(&mut self) -> *mut c_void { self.as_raw_mut() }
	}

	boxed_ref! { StereoBM, crate::calib3d::StereoMatcherTraitConst, as_raw_StereoMatcher, crate::calib3d::StereoMatcherTrait, as_raw_mut_StereoMatcher }

	impl crate::calib3d::StereoBMTraitConst for StereoBM {
		#[inline] fn as_raw_StereoBM(&self) -> *const c_void { self.as_raw() }
	}

	impl crate::calib3d::StereoBMTrait for StereoBM {
		#[inline] fn as_raw_mut_StereoBM(&mut self) -> *mut c_void { self.as_raw_mut() }
	}

	boxed_ref! { StereoBM, crate::calib3d::StereoBMTraitConst, as_raw_StereoBM, crate::calib3d::StereoBMTrait, as_raw_mut_StereoBM }

	/// The base class for stereo correspondence algorithms.
	pub struct StereoMatcher {
		ptr: *mut c_void,
	}

	opencv_type_boxed! { StereoMatcher }

	impl Drop for StereoMatcher {
		#[inline]
		fn drop(&mut self) {
			unsafe { sys::cv_StereoMatcher_delete(self.as_raw_mut_StereoMatcher()) };
		}
	}

	unsafe impl Send for StereoMatcher {}

	/// Constant methods for [crate::calib3d::StereoMatcher]
	pub trait StereoMatcherTraitConst: core::AlgorithmTraitConst {
		fn as_raw_StereoMatcher(&self) -> *const c_void;

		#[inline]
		fn get_min_disparity(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoMatcher_getMinDisparity_const(self.as_raw_StereoMatcher(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn get_num_disparities(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoMatcher_getNumDisparities_const(self.as_raw_StereoMatcher(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn get_block_size(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoMatcher_getBlockSize_const(self.as_raw_StereoMatcher(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn get_speckle_window_size(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoMatcher_getSpeckleWindowSize_const(self.as_raw_StereoMatcher(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn get_speckle_range(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoMatcher_getSpeckleRange_const(self.as_raw_StereoMatcher(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn get_disp12_max_diff(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoMatcher_getDisp12MaxDiff_const(self.as_raw_StereoMatcher(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

	}

	/// Mutable methods for [crate::calib3d::StereoMatcher]
	pub trait StereoMatcherTrait: core::AlgorithmTrait + crate::calib3d::StereoMatcherTraitConst {
		fn as_raw_mut_StereoMatcher(&mut self) -> *mut c_void;

		/// Computes disparity map for the specified stereo pair
		///
		/// ## Parameters
		/// * left: Left 8-bit single-channel image.
		/// * right: Right image of the same size and the same type as the left one.
		/// * disparity: Output disparity map. It has the same size as the input images. Some algorithms,
		/// like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value
		/// has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map.
		#[inline]
		fn compute(&mut self, left: &impl ToInputArray, right: &impl ToInputArray, disparity: &mut impl ToOutputArray) -> Result<()> {
			input_array_arg!(left);
			input_array_arg!(right);
			output_array_arg!(disparity);
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoMatcher_compute_const__InputArrayR_const__InputArrayR_const__OutputArrayR(self.as_raw_mut_StereoMatcher(), left.as_raw__InputArray(), right.as_raw__InputArray(), disparity.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn set_min_disparity(&mut self, min_disparity: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoMatcher_setMinDisparity_int(self.as_raw_mut_StereoMatcher(), min_disparity, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn set_num_disparities(&mut self, num_disparities: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoMatcher_setNumDisparities_int(self.as_raw_mut_StereoMatcher(), num_disparities, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn set_block_size(&mut self, block_size: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoMatcher_setBlockSize_int(self.as_raw_mut_StereoMatcher(), block_size, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn set_speckle_window_size(&mut self, speckle_window_size: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoMatcher_setSpeckleWindowSize_int(self.as_raw_mut_StereoMatcher(), speckle_window_size, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn set_speckle_range(&mut self, speckle_range: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoMatcher_setSpeckleRange_int(self.as_raw_mut_StereoMatcher(), speckle_range, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn set_disp12_max_diff(&mut self, disp12_max_diff: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoMatcher_setDisp12MaxDiff_int(self.as_raw_mut_StereoMatcher(), disp12_max_diff, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

	}

	impl std::fmt::Debug for StereoMatcher {
		#[inline]
		fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
			f.debug_struct("StereoMatcher")
				.finish()
		}
	}

	boxed_cast_base! { StereoMatcher, core::Algorithm, cv_StereoMatcher_to_Algorithm }

	boxed_cast_descendant! { StereoMatcher, crate::calib3d::StereoBM, cv_StereoMatcher_to_StereoBM }

	boxed_cast_descendant! { StereoMatcher, crate::calib3d::StereoSGBM, cv_StereoMatcher_to_StereoSGBM }

	impl core::AlgorithmTraitConst for StereoMatcher {
		#[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() }
	}

	impl core::AlgorithmTrait for StereoMatcher {
		#[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() }
	}

	boxed_ref! { StereoMatcher, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm }

	impl crate::calib3d::StereoMatcherTraitConst for StereoMatcher {
		#[inline] fn as_raw_StereoMatcher(&self) -> *const c_void { self.as_raw() }
	}

	impl crate::calib3d::StereoMatcherTrait for StereoMatcher {
		#[inline] fn as_raw_mut_StereoMatcher(&mut self) -> *mut c_void { self.as_raw_mut() }
	}

	boxed_ref! { StereoMatcher, crate::calib3d::StereoMatcherTraitConst, as_raw_StereoMatcher, crate::calib3d::StereoMatcherTrait, as_raw_mut_StereoMatcher }

	/// The class implements the modified H. Hirschmuller algorithm [HH08](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_HH08) that differs from the original
	/// one as follows:
	///
	/// *   By default, the algorithm is single-pass, which means that you consider only 5 directions
	/// instead of 8. Set mode=StereoSGBM::MODE_HH in createStereoSGBM to run the full variant of the
	/// algorithm but beware that it may consume a lot of memory.
	/// *   The algorithm matches blocks, not individual pixels. Though, setting blockSize=1 reduces the
	/// blocks to single pixels.
	/// *   Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi
	/// sub-pixel metric from [BT98](https://docs.opencv.org/4.12.0/d0/de3/citelist.html#CITEREF_BT98) is used. Though, the color images are supported as well.
	/// *   Some pre- and post- processing steps from K. Konolige algorithm StereoBM are included, for
	/// example: pre-filtering (StereoBM::PREFILTER_XSOBEL type) and post-filtering (uniqueness
	/// check, quadratic interpolation and speckle filtering).
	///
	///
	/// Note:
	///    *   (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found
	///        at opencv_source_code/samples/python/stereo_match.py
	pub struct StereoSGBM {
		ptr: *mut c_void,
	}

	opencv_type_boxed! { StereoSGBM }

	impl Drop for StereoSGBM {
		#[inline]
		fn drop(&mut self) {
			unsafe { sys::cv_StereoSGBM_delete(self.as_raw_mut_StereoSGBM()) };
		}
	}

	unsafe impl Send for StereoSGBM {}

	impl StereoSGBM {
		/// Creates StereoSGBM object
		///
		/// ## Parameters
		/// * minDisparity: Minimum possible disparity value. Normally, it is zero but sometimes
		/// rectification algorithms can shift images, so this parameter needs to be adjusted accordingly.
		/// * numDisparities: Maximum disparity minus minimum disparity. The value is always greater than
		/// zero. In the current implementation, this parameter must be divisible by 16.
		/// * blockSize: Matched block size. It must be an odd number \>=1 . Normally, it should be
		/// somewhere in the 3..11 range.
		/// * P1: The first parameter controlling the disparity smoothness. See below.
		/// * P2: The second parameter controlling the disparity smoothness. The larger the values are,
		/// the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1
		/// between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor
		/// pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good
		/// P1 and P2 values are shown (like 8\*number_of_image_channels\*blockSize\*blockSize and
		/// 32\*number_of_image_channels\*blockSize\*blockSize , respectively).
		/// * disp12MaxDiff: Maximum allowed difference (in integer pixel units) in the left-right
		/// disparity check. Set it to a non-positive value to disable the check.
		/// * preFilterCap: Truncation value for the prefiltered image pixels. The algorithm first
		/// computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval.
		/// The result values are passed to the Birchfield-Tomasi pixel cost function.
		/// * uniquenessRatio: Margin in percentage by which the best (minimum) computed cost function
		/// value should "win" the second best value to consider the found match correct. Normally, a value
		/// within the 5-15 range is good enough.
		/// * speckleWindowSize: Maximum size of smooth disparity regions to consider their noise speckles
		/// and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the
		/// 50-200 range.
		/// * speckleRange: Maximum disparity variation within each connected component. If you do speckle
		/// filtering, set the parameter to a positive value, it will be implicitly multiplied by 16.
		/// Normally, 1 or 2 is good enough.
		/// * mode: Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming
		/// algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and
		/// huge for HD-size pictures. By default, it is set to false .
		///
		/// The first constructor initializes StereoSGBM with all the default parameters. So, you only have to
		/// set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter
		/// to a custom value.
		///
		/// ## C++ default parameters
		/// * min_disparity: 0
		/// * num_disparities: 16
		/// * block_size: 3
		/// * p1: 0
		/// * p2: 0
		/// * disp12_max_diff: 0
		/// * pre_filter_cap: 0
		/// * uniqueness_ratio: 0
		/// * speckle_window_size: 0
		/// * speckle_range: 0
		/// * mode: StereoSGBM::MODE_SGBM
		#[inline]
		pub fn create(min_disparity: i32, num_disparities: i32, block_size: i32, p1: i32, p2: i32, disp12_max_diff: i32, pre_filter_cap: i32, uniqueness_ratio: i32, speckle_window_size: i32, speckle_range: i32, mode: i32) -> Result<core::Ptr<crate::calib3d::StereoSGBM>> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoSGBM_create_int_int_int_int_int_int_int_int_int_int_int(min_disparity, num_disparities, block_size, p1, p2, disp12_max_diff, pre_filter_cap, uniqueness_ratio, speckle_window_size, speckle_range, mode, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			let ret = unsafe { core::Ptr::<crate::calib3d::StereoSGBM>::opencv_from_extern(ret) };
			Ok(ret)
		}

		/// Creates StereoSGBM object
		///
		/// ## Parameters
		/// * minDisparity: Minimum possible disparity value. Normally, it is zero but sometimes
		/// rectification algorithms can shift images, so this parameter needs to be adjusted accordingly.
		/// * numDisparities: Maximum disparity minus minimum disparity. The value is always greater than
		/// zero. In the current implementation, this parameter must be divisible by 16.
		/// * blockSize: Matched block size. It must be an odd number \>=1 . Normally, it should be
		/// somewhere in the 3..11 range.
		/// * P1: The first parameter controlling the disparity smoothness. See below.
		/// * P2: The second parameter controlling the disparity smoothness. The larger the values are,
		/// the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1
		/// between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor
		/// pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good
		/// P1 and P2 values are shown (like 8\*number_of_image_channels\*blockSize\*blockSize and
		/// 32\*number_of_image_channels\*blockSize\*blockSize , respectively).
		/// * disp12MaxDiff: Maximum allowed difference (in integer pixel units) in the left-right
		/// disparity check. Set it to a non-positive value to disable the check.
		/// * preFilterCap: Truncation value for the prefiltered image pixels. The algorithm first
		/// computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval.
		/// The result values are passed to the Birchfield-Tomasi pixel cost function.
		/// * uniquenessRatio: Margin in percentage by which the best (minimum) computed cost function
		/// value should "win" the second best value to consider the found match correct. Normally, a value
		/// within the 5-15 range is good enough.
		/// * speckleWindowSize: Maximum size of smooth disparity regions to consider their noise speckles
		/// and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the
		/// 50-200 range.
		/// * speckleRange: Maximum disparity variation within each connected component. If you do speckle
		/// filtering, set the parameter to a positive value, it will be implicitly multiplied by 16.
		/// Normally, 1 or 2 is good enough.
		/// * mode: Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming
		/// algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and
		/// huge for HD-size pictures. By default, it is set to false .
		///
		/// The first constructor initializes StereoSGBM with all the default parameters. So, you only have to
		/// set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter
		/// to a custom value.
		///
		/// ## Note
		/// This alternative version of [StereoSGBM::create] function uses the following default values for its arguments:
		/// * min_disparity: 0
		/// * num_disparities: 16
		/// * block_size: 3
		/// * p1: 0
		/// * p2: 0
		/// * disp12_max_diff: 0
		/// * pre_filter_cap: 0
		/// * uniqueness_ratio: 0
		/// * speckle_window_size: 0
		/// * speckle_range: 0
		/// * mode: StereoSGBM::MODE_SGBM
		#[inline]
		pub fn create_def() -> Result<core::Ptr<crate::calib3d::StereoSGBM>> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoSGBM_create(ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			let ret = unsafe { core::Ptr::<crate::calib3d::StereoSGBM>::opencv_from_extern(ret) };
			Ok(ret)
		}

	}

	/// Constant methods for [crate::calib3d::StereoSGBM]
	pub trait StereoSGBMTraitConst: crate::calib3d::StereoMatcherTraitConst {
		fn as_raw_StereoSGBM(&self) -> *const c_void;

		#[inline]
		fn get_pre_filter_cap(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoSGBM_getPreFilterCap_const(self.as_raw_StereoSGBM(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn get_uniqueness_ratio(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoSGBM_getUniquenessRatio_const(self.as_raw_StereoSGBM(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn get_p1(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoSGBM_getP1_const(self.as_raw_StereoSGBM(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn get_p2(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoSGBM_getP2_const(self.as_raw_StereoSGBM(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn get_mode(&self) -> Result<i32> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoSGBM_getMode_const(self.as_raw_StereoSGBM(), ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

	}

	/// Mutable methods for [crate::calib3d::StereoSGBM]
	pub trait StereoSGBMTrait: crate::calib3d::StereoMatcherTrait + crate::calib3d::StereoSGBMTraitConst {
		fn as_raw_mut_StereoSGBM(&mut self) -> *mut c_void;

		#[inline]
		fn set_pre_filter_cap(&mut self, pre_filter_cap: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoSGBM_setPreFilterCap_int(self.as_raw_mut_StereoSGBM(), pre_filter_cap, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn set_uniqueness_ratio(&mut self, uniqueness_ratio: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoSGBM_setUniquenessRatio_int(self.as_raw_mut_StereoSGBM(), uniqueness_ratio, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn set_p1(&mut self, p1: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoSGBM_setP1_int(self.as_raw_mut_StereoSGBM(), p1, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn set_p2(&mut self, p2: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoSGBM_setP2_int(self.as_raw_mut_StereoSGBM(), p2, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

		#[inline]
		fn set_mode(&mut self, mode: i32) -> Result<()> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_StereoSGBM_setMode_int(self.as_raw_mut_StereoSGBM(), mode, ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

	}

	impl std::fmt::Debug for StereoSGBM {
		#[inline]
		fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
			f.debug_struct("StereoSGBM")
				.finish()
		}
	}

	boxed_cast_base! { StereoSGBM, core::Algorithm, cv_StereoSGBM_to_Algorithm }

	boxed_cast_base! { StereoSGBM, crate::calib3d::StereoMatcher, cv_StereoSGBM_to_StereoMatcher }

	impl core::AlgorithmTraitConst for StereoSGBM {
		#[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() }
	}

	impl core::AlgorithmTrait for StereoSGBM {
		#[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() }
	}

	boxed_ref! { StereoSGBM, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm }

	impl crate::calib3d::StereoMatcherTraitConst for StereoSGBM {
		#[inline] fn as_raw_StereoMatcher(&self) -> *const c_void { self.as_raw() }
	}

	impl crate::calib3d::StereoMatcherTrait for StereoSGBM {
		#[inline] fn as_raw_mut_StereoMatcher(&mut self) -> *mut c_void { self.as_raw_mut() }
	}

	boxed_ref! { StereoSGBM, crate::calib3d::StereoMatcherTraitConst, as_raw_StereoMatcher, crate::calib3d::StereoMatcherTrait, as_raw_mut_StereoMatcher }

	impl crate::calib3d::StereoSGBMTraitConst for StereoSGBM {
		#[inline] fn as_raw_StereoSGBM(&self) -> *const c_void { self.as_raw() }
	}

	impl crate::calib3d::StereoSGBMTrait for StereoSGBM {
		#[inline] fn as_raw_mut_StereoSGBM(&mut self) -> *mut c_void { self.as_raw_mut() }
	}

	boxed_ref! { StereoSGBM, crate::calib3d::StereoSGBMTraitConst, as_raw_StereoSGBM, crate::calib3d::StereoSGBMTrait, as_raw_mut_StereoSGBM }

	#[repr(C)]
	#[derive(Copy, Clone, Debug, PartialEq)]
	pub struct UsacParams {
		pub confidence: f64,
		pub is_parallel: bool,
		pub lo_iterations: i32,
		pub lo_method: crate::calib3d::LocalOptimMethod,
		pub lo_sample_size: i32,
		pub max_iterations: i32,
		pub neighbors_search: crate::calib3d::NeighborSearchMethod,
		pub random_generator_state: i32,
		pub sampler: crate::calib3d::SamplingMethod,
		pub score: crate::calib3d::ScoreMethod,
		pub threshold: f64,
		pub final_polisher: crate::calib3d::PolishingMethod,
		pub final_polisher_iterations: i32,
	}

	opencv_type_simple! { crate::calib3d::UsacParams }

	impl UsacParams {
		#[inline]
		pub fn default() -> Result<crate::calib3d::UsacParams> {
			return_send!(via ocvrs_return);
			unsafe { sys::cv_UsacParams_UsacParams(ocvrs_return.as_mut_ptr()) };
			return_receive!(ocvrs_return => ret);
			let ret = ret.into_result()?;
			Ok(ret)
		}

	}

}
